57 research outputs found
Performance of Sampling/Resampling-based Particle Filters Applied to Non-Linear Problems
In this work, we propose a wireless body area sensor network (WBASN) to monitor patient position. Localization and tracking are enhanced by improving the effect of the received signal strength (RSS) variation. First, we propose a modified particle filter (PF) that adjusts resampling parameters for the Kullback-Leibler distance (KLD)-resampling algorithm to ameliorate the effect of RSS variation by generating a sample set near the high-likelihood region. The key issue of this method is to use a resampling parameter lower bound for reducing both the root mean square error (RMSE) and the mean number of particles used. To determine this lower bound, an optimal algorithm is proposed based on the maximum RMSE between the proposed algorithm and the KLD-resampling algorithm or based on the maximum mean number of particles used of these algorithms. Finally, PFs based on KLD-sampling and KLD-resampling are proposed to minimize the efficient number of particles and to reduce the estimation error compared to traditional algorithms
Fight sample degeneracy and impoverishment in particle filters: A review of intelligent approaches
During the last two decades there has been a growing interest in Particle Filtering (PF). However, PF suffers from two long-standing problems that are referred to as sample degeneracy and impoverishment. We are investigating methods that are particularly efficient at Particle Distribution Optimization (PDO) to fight sample degeneracy and impoverishment, with an emphasis on intelligence choices. These methods benefit from such methods as Markov Chain Monte Carlo methods, Mean-shift algorithms, artificial intelligence algorithms (e.g., Particle Swarm Optimization, Genetic Algorithm and Ant Colony Optimization), machine learning approaches (e.g., clustering, splitting and merging) and their hybrids, forming a coherent standpoint to enhance the particle filter. The working mechanism, interrelationship, pros and cons of these approaches are provided. In addition, approaches that are effective for dealing with high-dimensionality are reviewed. While improving the filter performance in terms of accuracy, robustness and convergence, it is noted that advanced techniques employed in PF often causes additional computational requirement that will in turn sacrifice improvement obtained in real life filtering. This fact, hidden in pure simulations, deserves the attention of the users and designers of new filters
Text Similarity Between Concepts Extracted from Source Code and Documentation
Context: Constant evolution in software systems often results in its documentation losing sync with the content of the source code. The traceability research field has often helped in the past with the aim to recover links between code and documentation, when the two fell out of sync. Objective: The aim of this paper is to compare the concepts contained within the source code of a system with those extracted from its documentation, in order to detect how similar these two sets are. If vastly different, the difference between the two sets might indicate a considerable ageing of the documentation, and a need to update it. Methods: In this paper we reduce the source code of 50 software systems to a set of key terms, each containing the concepts of one of the systems sampled. At the same time, we reduce the documentation of each system to another set of key terms. We then use four different approaches for set comparison to detect how the sets are similar. Results: Using the well known Jaccard index as the benchmark for the comparisons, we have discovered that the cosine distance has excellent comparative powers, and depending on the pre-training of the machine learning model. In particular, the SpaCy and the FastText embeddings offer up to 80% and 90% similarity scores. Conclusion: For most of the sampled systems, the source code and the documentation tend to contain very similar concepts. Given the accuracy for one pre-trained model (e.g., FastText), it becomes also evident that a few systems show a measurable drift between the concepts contained in the documentation and in the source code.</p
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain
The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio
Application of deep learning methods in materials microscopy for the quality assessment of lithium-ion batteries and sintered NdFeB magnets
Die Qualitätskontrolle konzentriert sich auf die Erkennung von Produktfehlern und die Überwachung von Aktivitäten, um zu überprüfen, ob die Produkte den gewünschten Qualitätsstandard erfüllen. Viele Ansätze für die Qualitätskontrolle verwenden spezialisierte Bildverarbeitungssoftware, die auf manuell entwickelten Merkmalen basiert, die von Fachleuten entwickelt wurden, um Objekte zu erkennen und Bilder zu analysieren. Diese Modelle sind jedoch mühsam, kostspielig in der Entwicklung und schwer zu pflegen, während die erstellte Lösung oft spröde ist und für leicht unterschiedliche Anwendungsfälle erhebliche Anpassungen erfordert. Aus diesen Gründen wird die Qualitätskontrolle in der Industrie immer noch häufig manuell durchgeführt, was zeitaufwändig und fehleranfällig ist. Daher schlagen wir einen allgemeineren datengesteuerten Ansatz vor, der auf den jüngsten Fortschritten in der Computer-Vision-Technologie basiert und Faltungsneuronale Netze verwendet, um repräsentative Merkmale direkt aus den Daten zu lernen. Während herkömmliche Methoden handgefertigte Merkmale verwenden, um einzelne Objekte zu erkennen, lernen Deep-Learning-Ansätze verallgemeinerbare Merkmale direkt aus den Trainingsproben, um verschiedene Objekte zu erkennen.
In dieser Dissertation werden Modelle und Techniken für die automatisierte Erkennung von Defekten in lichtmikroskopischen Bildern von materialografisch präparierten Schnitten entwickelt. Wir entwickeln Modelle zur Defekterkennung, die sich grob in überwachte und unüberwachte Deep-Learning-Techniken einteilen lassen. Insbesondere werden verschiedene überwachte Deep-Learning-Modelle zur Erkennung von Defekten in der Mikrostruktur von Lithium-Ionen-Batterien entwickelt, von binären Klassifizierungsmodellen, die auf einem Sliding-Window-Ansatz mit begrenzten Trainingsdaten basieren, bis hin zu komplexen Defekterkennungs- und Lokalisierungsmodellen, die auf ein- und zweistufigen Detektoren basieren. Unser endgültiges Modell kann mehrere Klassen von Defekten in großen Mikroskopiebildern mit hoher Genauigkeit und nahezu in Echtzeit erkennen und lokalisieren.
Das erfolgreiche Trainieren von überwachten Deep-Learning-Modellen erfordert jedoch in der Regel eine ausreichend große Menge an markierten Trainingsbeispielen, die oft nicht ohne weiteres verfügbar sind und deren Beschaffung sehr kostspielig sein kann. Daher schlagen wir zwei Ansätze vor, die auf unbeaufsichtigtem Deep Learning zur Erkennung von Anomalien in der Mikrostruktur von gesinterten NdFeB-Magneten basieren, ohne dass markierte Trainingsdaten benötigt werden. Die Modelle sind in der Lage, Defekte zu erkennen, indem sie aus den Trainingsdaten indikative Merkmale von nur "normalen" Mikrostrukturmustern lernen. Wir zeigen experimentelle Ergebnisse der vorgeschlagenen Fehlererkennungssysteme, indem wir eine Qualitätsbewertung an kommerziellen Proben von Lithium-Ionen-Batterien und gesinterten NdFeB-Magneten durchführen
Autoencoder-based techniques for improved classification in settings with high dimensional and small sized data
Neural network models have been widely tested and analysed usinglarge sized high dimensional datasets. In real world application prob-lems, the available datasets are often limited in size due to reasonsrelated to the cost or difficulties encountered while collecting the data.This limitation in the number of examples may challenge the clas-sification algorithms and degrade their performance. A motivatingexample for this kind of problem is predicting the health status of atissue given its gene expression, when the number of samples availableto learn from is very small.Gene expression data has distinguishing characteristics attracting themachine learning research community. The high dimensionality ofthe data is one of the integral features that has to be considered whenbuilding predicting models. A single sample of the data is expressedby thousands of gene expressions compared to the benchmark imagesand texts that only have a few hundreds of features and commonlyused for analysing the existing models. Gene expression data samplesare also distributed unequally among the classes; in addition, theyinclude noisy features which degrade the prediction accuracy of themodels. These characteristics give rise to the need for using effec-tive dimensionality reduction methods that are able to discover thecomplex relationships between the features such as the autoencoders.
This thesis investigates the problem of predicting from small sizedhigh dimensional datasets by introducing novel autoencoder-basedtechniques to increase the classification accuracy of the data. Twoautoencoder-based methods for generating synthetic data examplesand synthetic representations of the data were respectively introducedin the first stage of the study. Both of these methods are applicableto the testing phase of the autoencoder and showed successful in in-creasing the predictability of the data.Enhancing the autoencoder’s ability in learning from small sized im-balanced data was investigated in the second stage of the projectto come up with techniques that improved the autoencoder’s gener-ated representations. Employing the radial basis activation mecha-nism used in radial-basis function networks, which learn in a super-vised manner, was a solution provided by this thesis to enhance therepresentations learned by unsupervised algorithms. This techniquewas later applied to stochastic variational autoencoders and showedpromising results in learning discriminating representations from thegene expression data.The contributions of this thesis can be described by a number of differ-ent methods applicable to different stages (training and testing) anddifferent autoencoder models (deterministic and stochastic) which, in-dividually, allow for enhancing the predictability of small sized highdimensional datasets compared to well known baseline methods
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
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