Nasir al-Din al-Tusi's understanding of trigonometry

The methodology adopted in this chapter is library and internet based. The data used were collected from dependable sources. The objective of this chapter is to investigate the understanding of Nasir aI-Din al-Tusi's work in trigonometry and to derive and analyze the scope of sine law which initiated by al - Tusi. The significance of this chapter is the usefulness of the sine law to various fields such as astronomy, geography and medicine. Trigonometry is a branch of mathematics concerned with specific functions of angles and their application to calculations. It is derived from two Greek words trigon meaning "triangle" and metron meaning "a measurement". Hence, it is a methodology for finding some unknown elements of triangle provided the data include a sufficient amount of linear and angular measurements to define a shap

Contributions of Ashraf Ali Thanwi to mental disease treatment

The methodology adopted in this chapter is library and internet based. The data used were collected from dependable sources. The understanding ofAshraf Ali Thanwi work in psychology was investigated and the scope is to examine and explain Islamic psychology as viewed and practiced for treatment of maladjusted person by Asharf Ali Thanwis. The significance of this chapter is the usefulness of Thanwi's methodology in healing through Islamic perspective. Psychology is the science of the mind and behavior. The word "psychology" comes from the Greek word psyche meaning "breathe, spirit, soul", and the Greek word logia meaning the study of something. According to Medilexicon's Medical Dictionary, psychology is "The profession (clinical psychology), scholarly discipline (academic psychology), and science (research psychology) concerned with the behavior of humans and animals, and related mental and physiologic processes" (http://www.medilexicon.com!medicaldictionary.php)

Hybrid algorithm for NARX network parameters' determination using differential evolution and genetic algorithm

A hybrid optimization algorithm using Differential Evolution (DE) and Genetic Algorithm (GA) is proposed in this study to address the problem of network parameters determination associated with the Nonlinear Autoregressive with eXogenous inputs Network (NARX-network). The proposed algorithm involves a two level optimization scheme to search for both optimal network architecture and weights. The DE at the upper level is formulated as combinatorial optimization to search for the network architecture while the associated network weights that minimize the prediction error is provided by the GA at the lower level. The performance of the algorithm is evaluated on identification of a laboratory rotary motion system. The system identification results show the effectiveness of the proposed algorithm for nonparametric model development

Development Of A Cloud-Based Condition Monitoring Scheme For Distribution Transformer Protection

Distribution transformers are a necessity to ensure a reliable power supply to consumers and their inability to function properly or even breakdown should be avoided due to the high cost of replacing them. Distribution transformers are large in numbers and randomly distributed in cities and there is a need to accurately monitor their daily/hourly performance. To achieve this, real-time monitoring of the transformer’s health status is proposed rather than the use of the traditional approach involving physical inspection and testing which is slow, tedious and time-consuming. This paper presents a cloud-based monitoring scheme applied to a prototype distribution transformer. A 10kVA, 0.415 kV prototype distribution transformer has been acquired and connected to three residences for data acquisition. A data acquisition system has been developed to monitor and record the parameters of the prototype transformer for 14 days.  The parameters, monitored in real-time include load current, phase voltage, transformer oil level, ambient temperature and oil temperature. The acquired real-time data of the transformer is validated with the standard measuring instrument. An algorithm was developed to transmit and log the data to ThinkSpeak cloud server via node MCU (ESP 8266). Results obtained in this study, which can be visualized via the graphical user interface of ThinkSpeak, indicate that the proposed scheme can acquire vital data from the distribution transformers and transmit the information to the monitoring centre

Development Of A Cloud-Based Condition Monitoring Scheme For Distribution Transformer Protection

Distribution transformers are a necessity to ensure a reliable power supply to consumers and their inability to function properly or even breakdown should be avoided due to the high cost of replacing them. Distribution transformers are large in numbers and randomly distributed in cities and there is a need to accurately monitor their daily/hourly performance. To achieve this, real-time monitoring of the transformer’s health status is proposed rather than the use of the traditional approach involving physical inspection and testing which is slow, tedious and time-consuming. This paper presents a cloud-based monitoring scheme applied to a prototype distribution transformer. A 10kVA, 0.415 kV prototype distribution transformer has been acquired and connected to three residences for data acquisition. A data acquisition system has been developed to monitor and record the parameters of the prototype transformer for 14 days.  The parameters, monitored in real-time include load current, phase voltage, transformer oil level, ambient temperature and oil temperature. The acquired real-time data of the transformer is validated with the standard measuring instrument. An algorithm was developed to transmit and log the data to ThinkSpeak cloud server via node MCU (ESP 8266). Results obtained in this study, which can be visualized via the graphical user interface of ThinkSpeak, indicate that the proposed scheme can acquire vital data from the distribution transformers and transmit the information to the monitoring centre

Consumer load prediction based on NARX for electricity theft detection

A range of load prediction techniques has largely been used for energy management at various levels. However, the data used for the prediction are cumulative energy data, which reveal the activities of consumers and not individual consumer, on the distribution power network. Individual consumer data is essential for real time prediction, monitoring and detect of electricity theft. A new approach of monitoring individual consumer based on consumer load prediction using nonlinear autoregressive with eXogenous input (NARX) network is considered in this study. One month average energy consumption data acquired from consumer load prototype developed was used. Consequently, 5-minute step ahead load prediction was achieved. The NARX architecture was based on nine hidden neurons and two tapped delay and the network trained using Bayesian regulation backpropagation technique. The data set contains a total of 8928 data points representing energy consumed at five minute interval for one month. The data was divided into two sets at ratio 70:30 for training and validation, respectively. The training data equals 6206 while the validation data is 2722. MATLAB environment was used for the processing of the data. The training and validation MSE is 0.0225 and 0.0533 respectively, while the total time for the training is 0.016s