53 research outputs found
Featured Collectives: Myanmar Street Photographers Debut - Invisible Photographer Asia
No full textaung-pyae-soe_sagaing_mandalay_myanmar_2015_02 Myanmar’s first organised group of Street Photographers will debut their exhibition and photobook titled ‘Featured Collectives’ at Myanmar Deitta in the country’s capital Yangon on 17th September 2016. Featured photographers include: Aung Khant, Aung Zaw Myo, Chan Nyein Aung, Chit Min Maung, Hein Htet, Lamin Oo, Min Zayar, Moe Myint San, Myat Thu, Naing Lin Soe, Nyein Su Wai Kyaw Soe, Phyo Hein Kyaw, Phyo Thiha, Sai Aung Main, Thant Zaw, Thet Hto..
Mixed materials from landfill as caisson infill
No full textThe report delves into the feasibility of employing mixed materials as alternative infill substances for caissons in land reclamation initiatives. The primary objective is to ascertain the suitability of these materials and predict the durability of caissons through a comprehensive investigation of chloride ingress and corrosion initiation on embedded steel reinforcements.
The methodology entails the Rapid Chloride Migration Test applied to local caisson concrete design mixes. This method assesses the chloride migration coefficient, providing insights into the rate of chloride ingress and the time required to reach critical chloride content on the reinforcement surface, initiating corrosion processes.
Key findings indicate that the migration coefficients fall within acceptable limits, affirming the suitability of the concrete design mix for caisson construction. The report underscores the significance of adopting a holistic approach, considering factors like chloride exposure, construction practices, and material characteristics when evaluating concrete durability.
Conclusions drawn from this study emphasize the need for a multifaceted perspective in concrete durability assessments. Advanced testing methods, modelling techniques, and predictive tools, including semi-probabilistic models, prove essential in offering more accurate service life predictions with acceptable levels of probability.
In terms of recommendations, the report underscores the importance of continued research and collaboration to unlock the full potential of mixed materials in enhancing concrete performance and durability. Efforts should concentrate on refining predictive tools, integrating emerging sustainable technologies, and disseminating knowledge to industry professionals. Through these initiatives, the widespread adoption of innovative and environmentally conscious practices can be fostered, ensuring the enduring resilience of concrete structures in an ever-evolving world.Bachelor of Engineering (Civil
TwitPre: Tweets Preprocessing Tool for Social Media Analysis
No full textEver escalating usage of social media brings asa powerful communication and information sharingtool used by millions of people around the world topost how they feel and what is happening now. It turninto a potential source of crowd wisdom extractionespecially in terms of sentiments analysis and opinionmining which lead to a significant task of currentresearch area. Major challenges in this area is to tamethe data in terms of noise, relevance, emoticons,folksonomies and slangs. TwitPre offer a regularexpression based preprocessing tasks on tweets.Expressions are defined according to the outcomes ofelegant analysis on twitter data. Experiments werecarried out to observe the effect of proposed tool whichclearly indicates the improvements in accuracycompared with the existing baseline
Rule-based Classification and Outlier Replacement for Daily Electricity Load Forecasting
No full textThis paper presents rule-based classification and outlier replacement for data arrangement and preprocess for daily electricity load forecasting. The historical load data from 2019 to 2021 are provided by the Electricity Generating Authority of Thailand (EGAT) and there are 48 periods in each day. The training data from 2019 to 2020 and the test data from 1st January 2021 to 31st December 2021 are applied. In both datasets, the independent variables include historical load (one day before, load one week before, and two weeks before). The rule-based classification is presented where the data is classified manually to group similar load patterns and cleaned by replacing holidays, bridging holidays, and outliers. The mean absolute percentage is used to evaluate the performance of the proposed model. We use the MLR, SVR, XGBoost, and NN models to apply this approach. Furthermore, we propose the ensemble approach that combines the strengths of these powerful models. The results show that the proposed MLR model is more effective in forecasting performance than others.</p
Effective analysis of emotion-based satire detection model on various machine learning algorithms
No full textEffective analysis of emotion-based satire detection model on various machine learning algorithms
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