9,859 research outputs found

    Detection of Single-Diode Model Characteristic Values from Measured Current-Voltage Curves for Online Condition Monitoring Purposes of Photovoltaic Power Systems

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    Aurinkosähkövoimalan aurinkokennot ovat järjestelmän elinkaaren myötä taipuvaisia ikääntymään ja rappeutumaan. Ikääntymisilmiöt aiheuttavat merkittäviä tehohäviöitä, mikä ilmenee taloudellisina tappioina. Näin ollen tarvitaan luotettava kunnonvalvontamenetelmä niin kennojen ikääntymisen toteamiseksi kuin ikääntymisen asteen määrittämiseksi. Tällaisia menetelmiä löytyy, mutta niiden käyttö on tyypillisesti työlästä ja kallista. Tämän väitöstutkimuksen tarkoitus on löytää ratkaisu kyseiseen ongelmaan. Käyttökelpoinen lähestymistapa aurinkosähkövoimalan kunnonvalvontaan on virta-jännitekäyrien mittaaminen aurinkopaneelin tai -paneelikokonaisuuden liittimistä ja aurinkokennon toimintaa kuvaavan matemaattisen mallin sovittaminen mitattuihin käyriin. Malliksi soveltuu laajasti käytetty yksidiodimalli. Mallin sovitteen parametrit tarjoavat diagnostisesti arvokasta tietoa aurinkokennojen kunnosta. Tässä on kuitenkin eräitä käytännön haasteita. Ensinnäkin yksidiodimallin parametreihin vaikuttavat myös toimintaolosuhteet, joiden mittauksia on harvoin käytännön aurinkosähkövoimaloissa. Täten on tarpeen tunnistaa toimintaolosuhteet laskennallisesti yhdessä yksidiodimallin parametrien kanssa. Toiseksi kokonaisten virta-jännitekäyrien käyttö sovitukseen vaatii voimalan alasajon mittausjakson ajaksi, kun taas osittaisten virta-jännitekäyrien käyttö heikentää sovitteen laatua. Näin ollen järjestelmällinen tutkimus mittausalueen rajoittamisen vaikutuksesta sovitteen antamiin parametreihin on tarpeen. Kolmanneksi virta-jännitemittausdatassa esiintyvät poikkeamat tekevät mittausalueen rajoittamisesta entistä hankalampaa. Täten tarvitaan sopiva mittausdatan esikäsittelymenetelmä. Näitä kysymyksiä käsitellään tässä väitöskirjassa kokeellisesta näkökulmasta. Ensiksi kehitetään uusi esikäsittelymenetelmä mitatuille virta-jännitekäyrille. Menetelmää voidaan käyttää parantamaan mittausdatan laatua, mikä tekee datan soveltuvammaksi yksidiodimallin sovittamiseen. Seuraavaksi kehitetään uusi yksidiodimallin sovitusmenetelmä, joka tunnistaa laskennallisesti varsinaisten yksidiodimallin parametrien ohella toimintalämpötilan ja -säteilyvoimakkuuden. Menetelmää voidaan käyttää täysin ilman ulkoisia säteilyvoimakkuus- ja lämpötilamittauksia. Lopuksi tarkastellaan järjestelmällisesti virta-jännitekäyrien mittausalueen rajoittamista maksimitehopisteen ympäristöön keskittyen lähinnä ikääntymisen tunnistamiseen ja näytetään, miten virta-jännitekäyrien mittausaluetta voidaan rajoittaa niin, että ikääntyminen saadaan yhä tunnistettua luotettavasti. Merkittävä tulos on, että kehitetty yksidiodimallin sovitusmenetelmä mahdollistaa sopivasti mitattujen osittaisten virtajännitekäyrien käytön parametrien tunnistamisessa.Photovoltaic (PV) power systems are prone to ageing and degradation occurring in the PV cells during their lifespan. Such phenomena cause significant output power degradation which manifests as economic losses. Hence, a reliable condition monitoring procedure to detect and quantify ageing is a necessity. Such procedures do exist, but they are typically laborious and costly. The present study aims at finding a solution for this problem. A feasible approach for monitoring the condition of a PV system is to measure the current-voltage curves from the terminals of a PV unit and fit a mathematical model describing the operation of a PV module or a larger PV unit to the curves. The widely used single-diode model is a suitable choice. The fitted model parameters provide valuable diagnostic information on the condition of the PV cells. However, there are some practical challenges. Firstly, the model parameters are affected by the operating conditions, the measurements of which seldom exist at practical PV sites. This makes it necessary to identify the operating conditions jointly with the model parameters. Secondly, using entire measured current-voltage curves in fitting requires the rundown of the PV system for the measurement period, while using partial current-voltage curves reduces the fit quality. Hence, a systematic study of the effect of the limitation of the measurement range on the fitted parameters is needed. Thirdly, the discrepancies in the current-voltage measurement data make such limitation even more involved. Hence a suitable pre-processing procedure for the measurement data is needed. These issues are addressed in this thesis from an empirical viewpoint. First, a new pre-processing procedure for the measured current-voltage curves is developed. It can be used to improve the quality of such measurement data, making it more suitable for fitting. Thereafter, a novel single-diode model fitting procedure identifying the operating irradiance and temperature jointly with the actual model parameters is developed. It can be utilized fully without external irradiance or temperature measurements. Finally, the effect of limiting the measurement range of the current-voltage curves to the vicinity of the maximum power point is systematically investigated, particularly focusing on ageing detection. It is shown how the measurement range of the current-voltage curves can be limited while maintaining the reliable detection of ageing. As a significant result, the developed single-diode model fitting procedure allows for the usage of suitably formed partial current-voltage curves in the parameter identification

    Neuromodulatory effects on early visual signal processing

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    Understanding how the brain processes information and generates simple to complex behavior constitutes one of the core objectives in systems neuroscience. However, when studying different neural circuits, their dynamics and interactions researchers often assume fixed connectivity, overlooking a crucial factor - the effect of neuromodulators. Neuromodulators can modulate circuit activity depending on several aspects, such as different brain states or sensory contexts. Therefore, considering the modulatory effects of neuromodulators on the functionality of neural circuits is an indispensable step towards a more complete picture of the brain’s ability to process information. Generally, this issue affects all neural systems; hence this thesis tries to address this with an experimental and computational approach to resolve neuromodulatory effects on cell type-level in a well-define system, the mouse retina. In the first study, we established and applied a machine-learning-based classification algorithm to identify individual functional retinal ganglion cell types, which enabled detailed cell type-resolved analyses. We applied the classifier to newly acquired data of light-evoked retinal ganglion cell responses and successfully identified their functional types. Here, the cell type-resolved analysis revealed that a particular principle of efficient coding applies to all types in a similar way. In a second study, we focused on the issue of inter-experimental variability that can occur during the process of pooling datasets. As a result, further downstream analyses may be complicated by the subtle variations between the individual datasets. To tackle this, we proposed a theoretical framework based on an adversarial autoencoder with the objective to remove inter-experimental variability from the pooled dataset, while preserving the underlying biological signal of interest. In the last study of this thesis, we investigated the functional effects of the neuromodulator nitric oxide on the retinal output signal. To this end, we used our previously developed retinal ganglion cell type classifier to unravel type-specific effects and established a paired recording protocol to account for type-specific time-dependent effects. We found that certain retinal ganglion cell types showed adaptational type-specific changes and that nitric oxide had a distinct modulation of a particular group of retinal ganglion cells. In summary, I first present several experimental and computational methods that allow to study functional neuromodulatory effects on the retinal output signal in a cell type-resolved manner and, second, use these tools to demonstrate their feasibility to study the neuromodulator nitric oxide

    Modelling bus bunching along a common line corridor considering passenger arrival time and transfer choice under stochastic travel time

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    This study examines bus bunching along a common-line corridor, considering crucial factors underexplored in existing literature, such as stochastic travel times, passenger arrival patterns, and passenger transfer behaviours. We first develop a bus motion model that captures the interaction between bus trajectories and passenger movement. Then we formulate a reliability-based passenger arrival time choice and a transfer choice model to characterise passengers’ behaviours. Afterwards, the bus motion model and the passenger choice models are integrated, and a Method of Successive Averages type iterative algorithm is developed to obtain stable passenger arrival patterns and transfer choices. Numerical experiments are carried out on a hypothetical network followed by a case with real-world data. Our findings demonstrate that a high transfer demand could amplify the propagation of bus bunching across lines along the common-line corridor. Meanwhile, a 50% increase in transfer demand leads to a 24%–30% rise in headway fluctuation. Furthermore, our results suggest that non-uniform passenger accumulation patterns can restore headway regularity as a result of coordinated passenger movement and bus motions, thus alleviating the persistent deterioration in bus bunching

    Modern computing: Vision and challenges

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    Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Performance and Competitiveness of Tree-Based Pipeline Optimization Tool

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    Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceAutomated machine learning (AutoML) is the process of automating the entire machine learn-ing workflow when applied to real-world problems. AutoML can increase data science produc-tivity while keeping the same performance and accuracy, allowing non-experts to use complex machine learning methods. Tree-based Pipeline Optimization Tool (TPOT) was one of the first AutoML methods created by data scientists and is targeted to optimize machine learning pipe-lines using genetic programming. While still under active development, TPOT is a very prom-ising AutoML tool. This Thesis aims to explore the algorithm and analyse its performance using real word data. Results show that evolution-based optimization is at least as accurate as TPOT initialization. The effectiveness of genetic operators, however, depends on the nature of the test case

    Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea

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    ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy action’s effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (−10% per year) trend after adoption and enforcement of these laws (β2 = 0.18, p-value = 0.07; β3 = −0.10, p-value = 0.02). SHS exposure at home (β2 = 0.10, p-value = 0.09; β3 = −0.03, p-value = 0.14) and the primary cigarette smoking rate (β2 = 0.03, p-value = 0.10; β3 = 0.008, p-value = 0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK

    On the path integration system of insects: there and back again

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    Navigation is an essential capability of animate organisms and robots. Among animate organisms of particular interest are insects because they are capable of a variety of navigation competencies solving challenging problems with limited resources, thereby providing inspiration for robot navigation. Ants, bees and other insects are able to return to their nest using a navigation strategy known as path integration. During path integration, the animal maintains a running estimate of the distance and direction to its nest as it travels. This estimate, known as the `home vector', enables the animal to return to its nest. Path integration was the technique used by sea navigators to cross the open seas in the past. To perform path integration, both sailors and insects need access to two pieces of information, their direction and their speed of motion over time. Neurons encoding the heading and speed have been found to converge on a highly conserved region of the insect brain, the central complex. It is, therefore, believed that the central complex is key to the computations pertaining to path integration. However, several questions remain about the exact structure of the neuronal circuit that tracks the animal's heading, how it differs between insect species, and how the speed and direction are integrated into a home vector and maintained in memory. In this thesis, I have combined behavioural, anatomical, and physiological data with computational modelling and agent simulations to tackle these questions. Analysis of the internal compass circuit of two insect species with highly divergent ecologies, the fruit fly Drosophila melanogaster and the desert locust Schistocerca gregaria, revealed that despite 400 million years of evolutionary divergence, both species share a fundamentally common internal compass circuit that keeps track of the animal's heading. However, subtle differences in the neuronal morphologies result in distinct circuit dynamics adapted to the ecology of each species, thereby providing insights into how neural circuits evolved to accommodate species-specific behaviours. The fast-moving insects need to update their home vector memory continuously as they move, yet they can remember it for several hours. This conjunction of fast updating and long persistence of the home vector does not directly map to current short, mid, and long-term memory accounts. An extensive literature review revealed a lack of available memory models that could support the home vector memory requirements. A comparison of existing behavioural data with the homing behaviour of simulated robot agents illustrated that the prevalent hypothesis, which posits that the neural substrate of the path integration memory is a bump attractor network, is contradicted by behavioural evidence. An investigation of the type of memory utilised during path integration revealed that cold-induced anaesthesia disrupts the ability of ants to return to their nest, but it does not eliminate their ability to move in the correct homing direction. Using computational modelling and simulated agents, I argue that the best explanation for this phenomenon is not two separate memories differently affected by temperature but a shared memory that encodes both the direction and distance. The results presented in this thesis shed some more light on the labyrinth that researchers of animal navigation have been exploring in their attempts to unravel a few more rounds of Ariadne's thread back to its origin. The findings provide valuable insights into the path integration system of insects and inspiration for future memory research, advancing path integration techniques in robotics, and developing novel neuromorphic solutions to computational problems
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