Browsing by Author "LHR TP 6413"

Now showing 1 - 2 of 2

Electrochemical study of Graphene/ Graphene Oxide/ Reduced Graphene Oxide in Fuel Cell
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2018) MUHAMMAD EHSAN; CIIT/FA15-RPH-034/LHR; Dr. Ghazanfar Abbas; LHR TP 6413
Graphene is of major importance in a very wide range of applications. Different works are under progress to make the products better in durability, performance and in other properties using graphene. In this work, the major objective was focused to use the graphene/graphene oxide/reduced graphene oxide into Solid Oxide Fuel Cell (SOFC) to reduce its operating/working temperatures with better performance. As SOFC operates at high temperature that is one of the major problems, so by using graphene/graphene oxide/reduced graphene oxide the performance and durability of SOFC will be under consideration at a temperature of < 600 °C. It could provide good performance in asymmetrical solid oxide fuel cell because it can be a good potential to be an alternative cathode material for LT-SOFCs. For the confirmation of results different characterization tests are performed including XRD, SEM, DC conductivity and Fuel cell performance. In this work the newly developed SOFC materials are analysed to check the performance/efficiency and find the applications in new energy devices. In this project, I prepared 12 nanocomposite anode materials for LT-SOFC. All samples were successfully synthesized by simple and cost effective methods (Sol-gel method and Solid State). The XRD results show that the synthesized materials are in nano scale. The structure of the all samples was Perovskite. The average crystalline size of all prepared sample lies in the range of 22-300 nm. The sample LNCZ + 30% GO shows the maximum electrochemical performance as compared to other compositions of graphene Oxide and graphene. The electrochemical performance of LNCZ + 30% GO was measured with OCV of 0.155V, current density 450 mAcm-2, power density 105 mW/cm-2 at 4000C with the hydrogen fuel. It shows better results as compared to graphene because it has dual phase composite structure and since its oxide material so GO treat batter with LNCZ-Oxide rather the graphene. The nanocomposite structures help to enhance the conductivity and performance of the cell at low temperature. So this nanocomposite material which have perovskite structure can be considered as better anode materials for LTSOFCs.
Neural Architecture Based Urdu Named Entity Recognition
(Library Information Services COMSATS University Islamaabad Lahore Campus, 2020) Muhammad Ehsan; FA18-RCS-018; LHR TP 6413; Dr. Muhammad Waqas Anwar
In natural language processing, named entity recognition (NER) is the issue of figuring out and arrange named elements referenced in unstructured content into pre-characterized classifications. To compare, evaluate and develop a NER system, we need standard and high-quality benchmark resources. But unfortunately, corpora in a huge amount are available for many other languages but not for the Urdu language. Even though Urdu is a rich language in all aspects and has more than 300 million speakers all over the world. As state-of-the-art sequence labeling systems are heavily based on large amounts of domain specific knowledge and hand-crafted feature for efficient learning from small data. When the dataset is in a large amount and high quality then the Named Entity Recognition (NER) task can be solved in a better way and provides better results on a large amount of labeled data, but the availability of labeled data in most of the languages is limited for training purpose. One way is there are scarcely any limitations on identifiable words. Hence, concluded the little amount of exampled data is troublesome. Subsequently, deliberately built orthographic features and language-explicit information assets (e.g., gazetteers) are broadly utilized for understanding this undertaking. Tragically, for new languages and new domains, the task of identifying explicit language sources and feature extraction is mainly an expensive process. As a result, it makes efficient NER system development adaptation a challenge. Another way to makes an efficient NER system is the use of deep neural network models that have achieved highest performance in the domain of natural language processing (NLP). However, this performance highly depends on the availability of huge amount of annotated data. The other way to get a better generalization from a small amount of data is to deploy semi-supervised models that performs on small set of annotated examples and a large set of unannotated data. Due to limited annotated data for many Natural Language Processing (NLP) problems like NER for the Urdu language, we will solve the NER problem using deep neural algorithms. In deep learning methods, we perform 44 experiments by using recurrent neural network (RNN), long short-term memory (LSTM), Bidirectional LSTM, gated recurrent unit (GRU), Bidirectional GRU and GRUCell in a supervised learning setting to compare with ix the previous models. Our model outperformed the previous results with F1-score of 91.43%. We also use multi-layer perceptron in a semi-supervised setting. This model gives the results with F1-score of 51.00%. We use standard evaluation measures to check the performance of the NER system. However, deep learning results with semi-supervised settings unable to beat the results of deep learning results with supervised settings.