Dissertationen zum Thema „NumpPy“

This master thesis deals with the comparison of an industrial system for object recognition and measurement with an affordable camera on the Raspberry Pie platform. The theoretical part briefly describes several methods for measuring dimensions, which are commonly used in industry, along with a general description of technical equipment for image recording. The next section describes 3 industrial systems for image analysis. The practical part of the work deals with the design of the measuring stand, the description of the technical equipment, the implementation of measurements using both platforms and the subsequent evaluation.

Background. Autodesk Maya allows scripting through both MEL and Python, and it is also possible to use different Python modules and a C++ API to perform the desired tasks. In theory, the C++ API is the fastest option in Maya, but there are no studies that support this claim. Other studies show that PyMEL is the slowest module in Maya to work with, but it is still the one used most frequently. This thesis has therefore made a speed measurement to determine which of the four selected Python modules and the C++ API is the fastest to use, regarding animation transfer between skeletal hierarchies with different numbers of data. Objectives. The aim of this thesis is to measure the performance in terms of speed of the Python modules NumPy, SciPy, OpenMaya and PyMEL, as well as the C++ API, in order to determine which is the fastest. Our objectives are to determine the speed performance of each module by conducting experiments. Methods. To achieve the objectives, an experiment was conducted to compare the speed of each Python module and the C++ API. To perform the experiments, the implementations for each module and the API have been written in the same way, with their own data types and classes. After performing the experiments for each module, the mean time consumption of each program has been compared. Results. The results from the experiments show that there was a noticeable difference in the speed between the C++ API and the Python modules, as the C++ API delivered the highest speed for all the skeletons that took place in the experiments. The OpenMaya module was the fastest Python module that was tested, while PyMEL was the slowest. The C++ API’s measurements show that it took 0,388–1,909 seconds depending on which skeleton was used to perform the experiment, while OpenMaya’s measurements were 0,538–3,119 seconds which show that OpenMaya is 39–68% slower than the C++ API. NumPy, SciPy and PyMEL’s measurements ranged from 689% to 3165% slower than the C++ API. Conclusions. The conclusion of the experiments show that the C++ API is the fastest to use, while PyMEL is the slowest module, as it is 2632–3165 % slower, when used for these animation transfers. This shows that the C++ API can be a better choice for complex calculations, such as animation transfers.

Studio di uno degli ecosistemi software oggi maggiormente utilizzati per Image Processing e Computer Vision. Lo studio affrontato parte dalle discipline di Image Processing e Computer Vision, passando per le principali soluzioni software adottate, tra le quali spiccano il linguaggio di programmazione ad alto livello Python e la libreria di Computer Vision OpenCV, per terminare con esempi pratici di base utili per capire il funzionamento delle tecnologie illustrate.

Researchers from Lund have conducted research on classifying images in three different categories, faces, landmarks and objects from EEG data [1]. The researchers used SVMs (Support Vector Machine) to classify between the three different categories [2, 3]. The scripts written to compute this had the potential to be extremely parallelized and could potentially be optimized to complete the computations much faster. The scripts were originally written in MATLAB which is a propriety software and not the most popular language for machine learning. The aim of this project is to translate the MATLAB code in the aforementioned Lund project to Python and perform code optimization and parallelization, in order to reduce the execution time. With much other data science transitioning into Python as well, it was a key part in this project to understand the differences between MATLAB and Python and how to translate MATLAB code to Python. With the exception of the preprocessing scripts, all the original MATLAB scripts were translated to Python. The translated Python scripts were optimized for speed and parallelized to decrease the execution time even further. Two major parallel implementations of the Python scripts were made. One parallel implementation was made using the Ray framework to compute in the cloud [4]. The other parallel implementation was made using the Accelerator, a framework to compute using local threads[5]. After translation, the code was tested versus the original results and profiled for any key mistakes, for example functions which took unnecessarily long time to execute. After optimization the single thread script was twelve times faster than the original MATLAB script. The final execution times were around 12−15 minutes, compared to the benchmark of 48 hours it is about 200 times faster. The benchmark of the original code used less iterations than the researchers used, decreasing the computational time from a week to 48 hours. The results of the project highlight the importance of learning and teaching basic profiling of slow code. While not entirely considered in this project, doing complexity analysis of code is important as well. Future work includes a deeper complexity analysis on both a high and low level, since a high level language such as Python relies heavily on modules with low level code. Future work also includes an in-depth analysis of the NumPy source code, as the current code relies heavily on NumPy and has shown tobe a bottleneck in this project.
Datorer är en central och oundviklig del av mångas vardag idag. De framsteg som har gjorts inom maskin-inlärning har gjort det nästintill lika viktigt inom mångas vardag som datorer. Med de otroliga framsteg som gjorts inom maskininlärning så har man börjat använda det för att försöka tolka hjärnsignaler, i hopp om att skapa BCI (Brain Computer Interface) eller hjärn dator gränssnitt. Forskare på Lund Universitet genomförde ett experiment där de försökte kategorisera hjärnsignaler med hjälp av maskininlärning. Forskarna försökte kategorisera mellan tre olika saker, objekt, ansikten och landmärken. En av de större utmaningarna med projektet var att det tog väldigt lång tid att beräkna på en vanlig dator, runt en veckas tid. Det här projektet hade som uppgift att försöka förbättra och snabba upp beräkningstiden av koden. Projektet översatte den kod som skulle förbättras från programmeringspråket MATLAB till Python. Projektet använde sig utav profilering, kluster och av ett accelereringsverktyg. Med hjälp av profilering kan man lokalisera delar av kod som körs långsamt och förbättra koden till att vara snabbare, ett optimeringsverktyg helt enkelt. Kluster är en samling av datorer som man kan använda för att kollektivt beräkna större problem med, för att öka beräkningshastigheten. Det här projektet använde sig utav ett ramverk kallat Ray, vilket möjliggjorde beräkningar av koden på ett kluster ägt av Ericsson. Ett accellereringsverktyg kallat the Accelerator implementerades också, separat från Ray implementationen av koden. The Accelerator utnyttjar endast lokala processorer för att parallelisera ett problem gentemot att använda flera datorer. Den största fördelen med the Accelerator är att den kan hålla reda på vad som beräknats och inte och sparar alla resultat automatiskt. När the Accelerator håller reda på allt så kan det återanvända gamla resultat till nya beräkningar ifall gammal kod används. Återanvändningen av gamla resultat betyder att man undviker beräkningstiden det skulle ta att beräkna kod man redan har beräknat. Detta projekt förbättrade beräkningshastigheten till att vara över två hundra gånger snabbare än den var innan. Med både Ray och the Accelerator sågs en förbättring på över två hundra gånger snabbare, med de bästa resultaten från the Accelerator på runt två hundra femtio gånger snabbare. Det skall dock nämnas att de bästa resultaten från the Accelerator gjordes på en bra server processor. En bra server processor är en stor investering medan en klustertjänst endast tar betalt för tiden man använder, vilket kan vara billigare på kort sikt. Om man däremot behöver använda datorkraften mycket kan det vara mer lönsamt i längden att använda en serverprocessor. En förbättring på två hundra gånger kan ha stora konsekvenser, om man kan se en sådan förbättring i hastighet för BCI överlag. Man skulle potentiellt kunna se en tolkning av hjärnsignaler mer i realtid, vilket man kunde använda till att styra apparater eller elektronik med. Resultaten i det här projektet har också visat att NumPy, ett vanligt beräknings bibliotek i Python, har saktat ned koden med de standardinställningar det kommer med. NumPy gjorde kod långsammare genom att använda flera trådar i processorn, även i en flertrådad miljö där manuell parallelisering hade gjorts. Det visade sig att NumPy var långsammare för både den fler och entrådade implementationen, vilket antyder att NumPy kan sakta ned kod generellt, något många är omedvetna om. Efter att manuellt fixat de miljövariabler som NumPy kommer med, så var koden mer än tre gånger så snabb än innan.

El objetivo del presente trabajo de investigación fue analizar el clima organizacional y su influencia en el desempeño laboral de los trabajadores de la generación Y de NUMAY S.A. en Lima Metropolitana durante el periodo 2018-2019. La investigación tuvo un enfoque mixto que combinó el estudio cuantitativo y el cualitativo. Para conseguir este objetivo, se hizo uso de dos instrumentos elaborados (encuesta de clima organizacional y de desempeño laboral), respetando cada una de las variables, se aplicó a 68 personas entre hombres y mujeres que trabajan en la empresa NUMAY S.A. que se encuentran dentro de un rango de edades estimado de 18 a 35 años, la denominada generación Y. Todos fueron evaluados con dos encuestas; una que mide el clima organizacional y otra de desempeño laboral, ambas de tipo Likert para ver la relación de las dos variables mencionadas. Además, la investigación se complementó de entrevistas semiestructuras aplicadas a especialistas de recursos humanos y a los principales representantes del caso de estudio. Se trabajó con el método descriptivo y un diseño descriptivo correlacional. A partir de ello se muestra que existe una relación entre el clima organizacional y desempeño laboral en la empresa NUMAY S.A., demostrada a través de la correlación de Pearson.

This master´s thesis deals with the calculation of parameters that would be able to differentiate healthy speech and speech impaired by hypokinetic dysarthria. There was staged hypokinetic dysarthria, which is a motoric disorder of speech and vocal tract. Were studied speech signal processing methods. Further parameters were studied, which could well differentiate healthy and diseased speech. Subsequently, these parameters were programmed in Python programming language. The next step was to create a web application in Django framework, which is used for the analysis of the dyzartic speech.

This thesis deals with the task of three-dimensional scene reconstruction using image data obtained from multiple views. It is assumed that intrinsic parameters of the utilized cameras are known. The theoretical chapters describe the basic priciples of individual reconstruction steps. Variuous possible implementaions of data model suitable for this task are also described. The practical part also includes a comparison of false keypoint correspondence filtering, implementation of polar stereo rectification and comparison of disparity map calculation methods that are bundled with the OpenCV library. In the final portion of the thesis, examples of reconstructed 3D models are presented and discussed.

Stress detection based on non-EEG physiological data can be useful for monitoring drivers, pilots, and also for monitoring of people in ordinary situation, where standard EEG monitoring is unsuitable. This work uses Non-EEG database freely available from Physionet. The database contains records of heart rate, saturation of blood oxygen, motion, a conductance of skin and temperature recorded for 3 type of stress alternated with relax state. Two final models were created in this thesis. First model for Binary classification stress/relax, second for classification of 4 different type of psychical state. Best results were reached using model created by decision tree algorithm with 8 features for binary classification and with 8 features for classification of 4 psychical state. Accuracy of final models is aproximately 95 % for binary model and 99 % for classification of 4 psychical state. All algorithms were implemented in Python.

This thesis investigates the function of number marking in Persian, within the framework of principles and parameters (P&P), and its relationship to inflectional and derivational number marking. Following the assumption in Distributed Morphology that inflectional and derivational morphology are not distinct, the distribution and properties of number marking in Persian provide evidence for both inflectional and derivational number marking. Assuming the two parameters of number marking (Wiltschko, 2007, 2008), number marking as a functional head and number marking as a modifier, I propose that number marking in Persian is mainly inflectional while number functions as a functional head; moreover, I propose that number marking in Persian can be derivational while number functions as a modifier. This explains that number morphology in Persian is not split to either inflectional or derivational. Rather, following Booij’s (1993, 1995) claim that inflectional morphology can be used contextually as well as inherently, I propose that number morphology in Persian is inflectional while number is a functional head; however, it has inherent residues as a modifier. Considering the functions of inflectional plural morphology in Persian, I argue that the functional category Number Phrase (NumP) is projected in Persian, and number is generated in the head of this functional category. Besides, Persian is a classifier language in which classifiers are in complementary distribution with plural marking. Following Borer’s (2005) discussion of the complementary distribution of plural marking and classifiers in Armenian, I argue that the head of NumP in Persian is either occupied by the plural maker or by full/empty classifiers. Moreover, I show that the presence of bare singulars/plurals in certain syntactic positions in Persian is related to the projection/non-projection of NumP.

Nella cosiddetta “era dei Big Data” le aziende sono sempre più guidate dai dati nel prendere decisioni strategiche, tanto che è stato coniato il termine data-drive companies per descrivere questo fenomeno. Tra le numerose tipologie di dati, l’acquisizione di quelli telemetrici, ovvero ottenuti a partire da sensori posizionati sulle macchine, si sta sempre più affermando grazie al paradigma dell’Industria 4.0. Il vantaggio principale consiste nella possibilità di utilizzare le informazioni ricavabili per aumentare la produttività degli impianti, in particolare grazie alla manutenzione predittiva. La trasformazione da dato in informazione ed infine in conoscenza non può però prescindere dalla presenza di metadati in grado di fornire alcune informazioni preliminari e riassuntive relative alla distribuzione dei dati e dalla qualità di questi ultimi. Il progetto realizzato presso l’ufficio IT di Bonfiglioli Riduttori S.p.A. ha proprio l’obiettivo di creare tabelle e dashboard in grado di raccogliere e presentare grandezze, tra cui media, deviazione standard, massimo e minimo, calcolate per le variabili telemetriche di coppia, corrente e potenza raccolte dall’azienda dalle proprie macchine e progettare un “engine” volto a verificare l’eventuale presenza di valori anomali, frutto di un possibile problema di mapping delle variabili. Gli output prodotti andranno a supportare l’implementazione della manutenzione predittiva sulle macchine presenti negli stabilimenti produttivi dell’azienda.

博士
國立臺灣大學
歷史學研究所
107
By using “Dao Qie Lei (Category of theft, 盜竊類)” of Qingdai Tongzhichao Sichuan Baoxian Yamen Dangan (Archive Ba, 清代同治朝四川巴縣衙門檔案) and other relative sources, this dissertation explores what issues a magistrate as the most fundamental official of the administrative bureaucracy should concern at the trail. This dissertation analyzes this through three aspects—the change of laws, the formation of official knowledge, and the reflection of the social realities—to understand the question of how a magistrate thinks by discussing the general and particular situations. This dissertation seeks three primary topics. The first is the change of laws. The legal system includes law, regulation, and provincial regulation to deal with different administrative levels due to the different considerations on the criminal action of theft. The second is the formation of official knowledge. Official handbooks and detective stories each represent what the image of an ideal official is for the officials and ordinaries. The third centers on Baxian magistrates’ dilemma between administrative cohorts and the diverse society. The magistrates of Baxian suffered from a relatively higher pressure because they located at the city of Chongqing with Chuandong Dao, the prefecture of Chongqing, and the garrison of Chongqing, although it was comparatively easier to transfer criminals among them. In addition, the magistrate of Baxian also encountered the influences from the diversity of Chongqing urban at the trail. During the reign of Tongzhi period, Chongqing was featured as an immigrant and commercial society. The fundamental organizations had active effects on local society. Meanwhile, theft existed during the wartime and post-war. Examining the spatial structure, Chongqing was not only crowded but also was framed as multiple areas by different organizations. Outside the urban area, there were villages located among mountainous areas. The city and villages were divided by the Yangzi and Jialing River. These spatial characteristics displayed the differences of theft between urban and countryside, and it also showed the different reactions while people encountered from theft. By focusing on illicit goods, this dissertation investigates on the Baxian magistrates’ struggles during the reign of Tongzhi period because complicated considerations entangled. They had to find out the illicit goods, obtained evidences, avoided framing up, counting the value of spoils, and transferring criminals to other administrative levels based on the severe process. Although these magistrates could play around the judgements on these cases, they still needed to obey the legal system. Therefore, this dissertation argues that magistrates’ trail was influenced by society, legal system, and their knowledge.