Photovoltaic performance enhancement in monocrystalline silicon solar cells

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe approaching depletion of fossil fuels and the increasingly serious global climate change have driven people to search for clean and renewable alternative energy sources. Photovoltaic (PV) cells, which allow the transformation of sunlight directly into electric energy, are expected to play an important role in addressing the above-mentioned challenges. Despite of this huge potentiality, these devices are suffering from the low efficiency and high generation cost of electricity. To meet the ever-growing demand for energy in a sustainable manner, the production of cost-effective device has become the urgent affairs for the development of solar industry. In this work, applications of effective titanium dioxide (TiO2)-based aerogels nanomaterials and rare earth cations-activated photoluminescent down-conversion phosphor materials to the domain of energy conversion, particularly for the photovoltaic performance enhancement of single-junction monocrystalline silicon (mono-Si) solar cells are investigated. Improved conversion efficiency in solar cell was demonstrated by developing novel and low-cost anti-reflection coatings (ARCs) on the cell’s textured surface through screen printing technique. TiO2 has been widely used in silicon PV devices as ARCs owing to its outstanding optical properties. This study looks at investigating the influence of high surface area anatase TiO2 based nanoaerogels for the light harvesting enhancement in solar cells, specifically at the ultraviolet (UV)-blue wavelengths of solar spectrum. Mesoporous TiO2 and magnesium oxide (MgO)-doped TiO2 aerogels were prepared using a precipitation method in conjunction with a modified sol-gel process. The anti-reflection coating (ARC) was formed by screen printing an optimised mixture solution comprising TiO2-based aerogels nanomaterials and a co-polymer resin of ethylene vinyl acetate (EVA) on the textured surface solar cell. The obtained results revealed an optimum relative enhancement of 6.0% in conversion efficiency for MgO-doped TiO2 coating and 3.4% for the undoped-TiO2 under a simulated one-sun illumination. Given that the silicon solar cells exhibit weak response to the short wavelength of incident light, an alternative ingenious approach to suppress the spectral mismatch and increase the device efficiency has been executed based on the method of spectrum modification by employing down-converting photoluminescent phosphor materials. Silicon PV cells were coated with a luminescent layer composed of EVA and high-quantum yield terbium-activated gadolinium oxysulfide (Gd2O2S:Tb3+) phosphor using rotary screen printing. The modified cells showed an optimum enhancement of 3.6% in conversion efficiency relative to those for a bare cell. The obtained results also demonstrated that the down-conversion (DC) effect induced by the doping agent (Tb3+) is solely responsible for the PV cells performance enhancement

A Topological Method for Comparing Document Semantics

Comparing document semantics is one of the toughest tasks in both Natural Language Processing and Information Retrieval. To date, on one hand, the tools for this task are still rare. On the other hand, most relevant methods are devised from the statistic or the vector space model perspectives but nearly none from a topological perspective. In this paper, we hope to make a different sound. A novel algorithm based on topological persistence for comparing semantics similarity between two documents is proposed. Our experiments are conducted on a document dataset with human judges' results. A collection of state-of-the-art methods are selected for comparison. The experimental results show that our algorithm can produce highly human-consistent results, and also beats most state-of-the-art methods though ties with NLTK.Comment: 9 pages, 3 tables, 9th International Conference on Natural Language Processing (NLP 2020

Optical Network Virtualisation using Multi-technology Monitoring and SDN-enabled Optical Transceiver

We introduce the real-time multi-technology transport layer monitoring to facilitate the coordinated virtualisation of optical and Ethernet networks supported by optical virtualise-able transceivers (V-BVT). A monitoring and network resource configuration scheme is proposed to include the hardware monitoring in both Ethernet and Optical layers. The scheme depicts the data and control interactions among multiple network layers under the software defined network (SDN) background, as well as the application that analyses the monitored data obtained from the database. We also present a re-configuration algorithm to adaptively modify the composition of virtual optical networks based on two criteria. The proposed monitoring scheme is experimentally demonstrated with OpenFlow (OF) extensions for a holistic (re-)configuration across both layers in Ethernet switches and V-BVTs

Experimental investigation of fatigue crack growth behavior of GH2036 under combined high and low cycle fatigue

International audienceFatigue crack growth rates have been experimentally determined for the superalloy GH2036 (in Chinese series) at an elevated temperature of 550°C under pure low cycle fatigue (LCF) and combined high and low cycle fatigue (CCF) loading conditions by establishing a CCF test rig and using corner-notched specimens. These studies reveal decelerated crack growth rates under CCF loading compared to pure LCF loading, and crack propagation accelerates as the dwell time prolongs. Then the mechanism of fatigue crack growth at different loadings has been discussed by using scanning electron microscope (SEM) analyses of the fracture surface

Word-level Textual Adversarial Attacking as Combinatorial Optimization

Adversarial attacks are carried out to reveal the vulnerability of deep neural networks. Textual adversarial attacking is challenging because text is discrete and a small perturbation can bring significant change to the original input. Word-level attacking, which can be regarded as a combinatorial optimization problem, is a well-studied class of textual attack methods. However, existing word-level attack models are far from perfect, largely because unsuitable search space reduction methods and inefficient optimization algorithms are employed. In this paper, we propose a novel attack model, which incorporates the sememe-based word substitution method and particle swarm optimization-based search algorithm to solve the two problems separately. We conduct exhaustive experiments to evaluate our attack model by attacking BiLSTM and BERT on three benchmark datasets. Experimental results demonstrate that our model consistently achieves much higher attack success rates and crafts more high-quality adversarial examples as compared to baseline methods. Also, further experiments show our model has higher transferability and can bring more robustness enhancement to victim models by adversarial training. All the code and data of this paper can be obtained on https://github.com/thunlp/SememePSO-Attack.Comment: Accepted at ACL 2020 as a long paper (a typo is corrected as compared with the official conference camera-ready version). 16 pages, 3 figure

Machine learning-based early diagnosis of autism according to eye movements of real and artificial faces scanning

BackgroundStudies on eye movements found that children with autism spectrum disorder (ASD) had abnormal gaze behavior to social stimuli. The current study aimed to investigate whether their eye movement patterns in relation to cartoon characters or real people could be useful in identifying ASD children.MethodsEye-tracking tests based on videos of cartoon characters and real people were performed for ASD and typically developing (TD) children aged between 12 and 60 months. A three-level hierarchical structure including participants, events, and areas of interest was used to arrange the data obtained from eye-tracking tests. Random forest was adopted as the feature selection tool and classifier, and the flattened vectors and diagnostic information were used as features and labels. A logistic regression was used to evaluate the impact of the most important features.ResultsA total of 161 children (117 ASD and 44 TD) with a mean age of 39.70 ± 12.27 months were recruited. The overall accuracy, precision, and recall of the model were 0.73, 0.73, and 0.75, respectively. Attention to human-related elements was positively related to the diagnosis of ASD, while fixation time for cartoons was negatively related to the diagnosis.ConclusionUsing eye-tracking techniques with machine learning algorithms might be promising for identifying ASD. The value of artificial faces, such as cartoon characters, in the field of ASD diagnosis and intervention is worth further exploring