Facing the growing problem of the electric power consumption in Egyptian residential building using building performance simulation program

Egypt has been experiencing recurrent power cuts especially at the summer, with the problem being made worse by the extra demands placed on the electrical grid by the advent of the holy month of Ramadan. Electricity shortages are now a problem in Cairo, Alexandria, Sohag, Qena, Luxor, Aswan, and Nubia, as well as in the Nile Delta governorates of Beheira and Qalioubiya. The aim of this study is to develop a model for the Egyptian residential building using Building Performance Simulation Program and make sensitivity analysis on some variables effecting the electric power consumption in order to help faceting the growing problem in Egypt. The model was created using the IES-VE 2012 (Integrated Environmental Solution ). The simulation model was verified against the survey data for the Egyptian apartment and same model simulated using energy Plus simulation tool. The results of the program describing different situations for energy using profile for the air conditions, lighting and equipments in respect to building layout and construction climate and pattern of use. This model can be used in the future to help in reducing the electric power consumption in the residential building

Arabic Spelling Correction using Supervised Learning

In this work, we address the problem of spelling correction in the Arabic language utilizing the new corpus provided by QALB (Qatar Arabic Language Bank) project which is an annotated corpus of sentences with errors and their corrections. The corpus contains edit, add before, split, merge, add after, move and other error types. We are concerned with the first four error types as they contribute more than 90% of the spelling errors in the corpus. The proposed system has many models to address each error type on its own and then integrating all the models to provide an efficient and robust system that achieves an overall recall of 0.59, precision of 0.58 and F1 score of 0.58 including all the error types on the development set. Our system participated in the QALB 2014 shared task "Automatic Arabic Error Correction" and achieved an F1 score of 0.6, earning the sixth place out of nine participants.Comment: System description paper that is submitted in the EMNLP 2014 conference shared task "Automatic Arabic Error Correction" (Mohit et al., 2014) in the Arabic NLP workshop. 6 page

A Machine Learning Approach For Opinion Holder Extraction In Arabic Language

Opinion mining aims at extracting useful subjective information from reliable amounts of text. Opinion mining holder recognition is a task that has not been considered yet in Arabic Language. This task essentially requires deep understanding of clauses structures. Unfortunately, the lack of a robust, publicly available, Arabic parser further complicates the research. This paper presents a leading research for the opinion holder extraction in Arabic news independent from any lexical parsers. We investigate constructing a comprehensive feature set to compensate the lack of parsing structural outcomes. The proposed feature set is tuned from English previous works coupled with our proposed semantic field and named entities features. Our feature analysis is based on Conditional Random Fields (CRF) and semi-supervised pattern recognition techniques. Different research models are evaluated via cross-validation experiments achieving 54.03 F-measure. We publicly release our own research outcome corpus and lexicon for opinion mining community to encourage further research

A study to explore the expressed needs of ten primiparous mothers during labour and delivery

Thesis (M.S.)--Boston Universit

CONTROL VALVES FAULT DETECTION AND MONITORING SYSTEM USING ACOUSTIC EMISSION

Since Control Valves have significant importance in any process plant As they are used in manipulating the flow rates through processes. As they are the key components in any control systems. Malfunctioning control valves will degrade the control loop performance and cause the unscheduled system shutdown thus, resulting in unnecessary economic losses. This Project is all about solving the above issue, it uses Acoustic Emission technique to monitor and detect faults in valves, through extensive studies elaborated in the literature review, this technique has proved to be efficient and successful in terms of detecting cracks, faults in different materials. Therefore it will be used in our project to develop a fault monitoring system for valves using acoustic emission equipment

Online learning for parameter selection in large scale image search

We explore using online learning for selecting the best parameters of Bag of Words systems when searching large scale image collections. We study two algorithms for no regret online learning: Hedge algorithm that works in the full information setting, and Exp3 that works in the bandit setting. We use these algorithms for parameter selection in two scenarios: (a) using a training set to obtain weights for the different parameters, then either choosing the parameter setting with maximum weight or combining their results with weighted majority vote; (b) working fully online by selecting a parameter combination at every time step. We demonstrate the usefulness of online learning using experiments on four different real world datasets