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Chaos-based grey wolf optimizer for higher order sliding mode position control of a robotic manipulator
No full textThe use of rigid robot manipulators with good performance in industrial applications demands a proper robust and optimized control technique. Several works have proven the efficient use of metaheuristics optimization algorithms to work with complex problems in the robotic area. In this work, it is proposed the use of Grey Wolf Optimizer (GWO) with chaotic basis to optimize the parameters of a robust Higher Order Sliding Modes (HOSM) controller for the position control in joint space of a rigid robot manipulator. A total of seven test cases were considered varying the chosen chaotic map, face to the original GWO and the general repeatability of such algorithm is improved using chaotic versions. Also, two cost functions were tested within the HOSM optimization. Simulation results suggest that both algorithm and cost function formulations influence the chaotic map choice. In fact, the chattering problem, presented by HOSM controllers, is reduced when the cost function attempts to minimize the total variation of the control signal
Analysis of the relationship between local climate change mitigation actions and greenhouse gas emissions - Empirical insights
No full textLocal actions are seen as of major importance for the achievement of climate change mitigation targets. In the past few years, the number of local action plans towards climate change mitigation has been increasing, and it is essential to analyze their contribution to the achievement of global targets. Even if the relationship between local action plans and the reduction of energy use and GHG emissions is often assumed, this has not yet been validated nor quantified by empirical studies involving a large number of municipalities. Thus, the aim of this paper is to. perform an empirical analysis on the link between local action plans and energy use and GHG emissions. The analysis is composed by a test of hypothesis and a regression analysis, performed for the municipalities of three European countries Portugal, Sweden and United Kingdom. The main conclusion is that, in the context of these three countries, the analysis performed was not able to detect a significant impact related to the existence of local plans on GHG emissions. From the panel data regression analysis, it was possible to confirm that external factors, not directly related to local climate change mitigation actions, have a significant impact on GHG emissions
Monitoring of oxidation phases of copper thin films using long period fiber gratings
No full textLong period fiber gratings (LPFGs) were used to monitor the characteristics of copper (Cu) thin films when annealed in air atmosphere up to similar to 680 degrees C. The wavelength and the optical power shift of the resonant bands of the LPFGs when coated with the Cu thin films, were measured as a function of the annealing temperature, and were found to exhibit a different evolution comparing to a bare LPFGs. Thin films of Cu deposited on quartz (SiO2) substrates were annealed and analyzed by XRD, SEM/EDS and Raman spectroscopy, allowing to identify the formation of two distinct oxide phases at different temperatures, cuprous (Cu2O-cuprite) and cupric (CuO-tenorite) oxides, respectively. The observed features of the resonant bands of the LPFGs were found to be associated with the Cu oxide phase transitions, indicating the possibility of using LPFGs to monitor, in real time, the oxidation states of Cu thin films by following specific characteristics of the attenuation bands. In addition, LPFGs over coated with the two distinct oxidation phases of Cu were characterized for refractive index sensing in the range between 1.300 to 1.600, leading to the conclusion that the sensitivity to the refractive index of the surrounding medium of Cu coated LPFGs sensing systems can be temperature tuned
Mistrustful P2P: Deterministic privacy-preserving P2P file sharing model to hide user content interests in untrusted peer-to-peer networks
Get PDFP2P networks endowed individuals with the means to easily and efficiently distribute digital media over the Internet, but user legal liability issues may be raised as they also facilitate the unauthorized distribution and reproduction of copyrighted material. Traditional P2P file sharing systems focus on performance and scalability, disregarding any privacy or legal issues that may arise from their use. Lacking alternatives, and unaware of the privacy issues that arise from relaying traffic of insecure applications, users have adopted anonymity systems for P2P file sharing. This work aims at hiding user content interests from malicious peers through plausible deniability. The Mistrustful P2P model is built on the concept of mistrusting all the entities participating in the P2P network, hence its name. It provides a deterministic and configurable privacy protection that relies on cover content downloads to hide user content interests, has no trust requirements, and introduces several mechanisms to prevent user legal liability and reduce network overhead while enabling timely content downloads. We extend previous work on the Mistrustful P2P model by discussing its legal and ethical framework, assessing its feasibility for more use cases, providing a security analysis, comparing it against a traditional P2P file sharing model, and further defining and improving its main mechanisms
WCDS: A Two-Phase Weightless Neural System for Data Stream Clustering
Get PDFClustering is a powerful and versatile tool for knowledge discovery, able to provide a valuable information for data analysis in various domains. To perform this task based on streaming data is quite challenging: outdated knowledge needs to be disposed while the current knowledge is obtained from fresh data; since data are continuously flowing, strict efficiency constraints have to be met. This paper presents WCDS, an approach to this problem based on the WiSARD artificial neural network model. This model already had useful characteristics as inherent incremental learning capability and patent functioning speed. These were combined with novel features as an adaptive countermeasure to cluster imbalance, a mechanism to discard expired data, and offline clustering based on a pairwise similarity measure for WiSARD discriminators. In an insightful experimental evaluation, the proposed system had an excellent performance according to multiple quality standards. This supports its applicability for the analysis of data streams
An evolutionary algorithm for clustering data streams with a variable number of clusters
No full textSeveral algorithms for clustering data streams based on k-Means have been proposed in the literature. However, most of them assume that the number of clusters, k, is known a priori by the user and can be kept fixed throughout the data analySis process. Besides the difficulty in choosing k, data stream clustering imposes several challenges to be addressed, such as addressing non-stationary, unbounded data that arrive in an online fashion. In this paper, we propose a Fast Evolutionary Algorithm for Clustering data streams (FEAC-Stream) that allows estimating k automatically from data in an online fashion. FEAC-Stream uses the Page-Hinkley Test to detect eventual degradation in the quality of the induced clusters, thereby triggering an evolutionary algorithm that re-estimates k accordingly. FEAC-Stream relies on the assumption that clusters of (partially unknown) data can provide useful information about the dynamics of the data stream. We illustrate the potential of FEAC-Stream in a set of experiments using both synthetic and real-world data streams, comparing it to four related algorithms, namely: CluStream-OMRk, CluStream-BkM, StreamKM++-OMRk and StreamKM++-BkM. The obtained results show that FEAC-Stream provides good data partitions and that it can detect, and accordingly react to, data changes
Sensing with ultra-short Fabry-Perot cavities written into optical micro-fibers
No full textThe small dimensions of optical fiber sensors are of particular interest to biological applications, given the ability to penetrate relatively inaccessible regions. However, conventional optical fibers are larger than biological material such as cells, and thus there is a need to further miniaturize fiber sensors. Here we present the fabrication of ultra-small Fabry-Perot cavities written into optical micro-fibers using focused ion beam milling. We have fabricated cavities as small as 2.8 pm and demonstrated their use for sensing of both bulk refractive index and thin-layer coatings. In order to achieve sensitive measurements we interrogate at visible wavelengths with a broadband detection system, thereby reducing the free spectral range of the interferometer relative to the measurement bandwidth, increasing the number of interference fringes, and allowing for the implementation of the Fourier shift method
Markov logic networks for adverse drug event extraction from text
Get PDFAdverse drug events (ADEs) are a major concern and point of emphasis for the medical profession, government, and society. A diverse set of techniques from epidemiology, statistics, and computer science are being proposed and studied for ADE discovery from observational health data (e.g., EHR and claims data), social network data (e.g., Google and Twitter posts), and other information sources. Methodologies are needed for evaluating, quantitatively measuring and comparing the ability of these various approaches to accurately discover ADEs. This work is motivated by the observation that text sources such as the Medline/Medinfo library provide a wealth of information on human health. Unfortunately, ADEs often result from unexpected interactions, and the connection between conditions and drugs is not explicit in these sources. Thus, in this work, we address the question of whether we can quantitatively estimate relationships between drugs and conditions from the medical literature. This paper proposes and studies a state-of-the-art NLP-based extraction of ADEs from text
