This paper presents L1-adaptive controller for controlling uncertain parameters and time-varying unknown parameters to control the position of a DC servomotor. For the purpose of comparison, the effectiveness of L1-adaptive controller for position control of studied servomotor has been examined and compared with another adaptive controller; Model Reference Adaptive Controller (MRAC). Robustness of both L1-adaptive controller and model reference adaptive controller to different input reference signals and different structures of uncertainty were studied. Three different types of input signals are taken into account; ramp, step and sinusoidal. The L1-adaptive controller ensured uniformly bounded transient and asymptotic tracking for both system's input and output signals, simultaneously with asymptotic tracking. Simulations of a DC servomotor with time-varying friction and disturbance are presented to verify the theoretical findings.
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The research aimed to identify “The impact of an instructional-learning design based on the brain- compatible model in systemic thinking among first intermediate grade female students in Mathematics”, in the day schools of the second Karkh Educational directorate.In order to achieve the research objective, the following null hypothesis was formulated:There is no statistically significant difference at the significance level (0.05) among the average scores of the experimental group students who will be taught by applying an (instructional- learning) design based to on the brain–compatible model and the average scores of the control group students who will be taught through the traditional method in the systemic thinking test.The resear
... Show MoreConvolutional Neural Networks (CNN) have high performance in the fields of object recognition and classification. The strength of CNNs comes from the fact that they are able to extract information from raw-pixel content and learn features automatically. Feature extraction and classification algorithms can be either hand-crafted or Deep Learning (DL) based. DL detection approaches can be either two stages (region proposal approaches) detector or a single stage (non-region proposal approach) detector. Region proposal-based techniques include R-CNN, Fast RCNN, and Faster RCNN. Non-region proposal-based techniques include Single Shot Detector (SSD) and You Only Look Once (YOLO). We are going to compare the speed and accuracy of Faster RCNN,
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This research was carried out at University of Baghdad - College of Agricultural Engineering Sciences during the fall season of 2020 and spring season of 2021 in order to evaluate the effect of organic fertilizer and the foliar application of boron on the growth and yield of industrial potatoes (Solanum tuberosum L.). Using factorial experiment (5*4) within Randomized Complete Block Design with three replicates, the organic fertilizer (palm fronds peat) was applied at four levels (0, 12, 24, and 36 ton ha-1) in addition to the treatment of the recommended of chemical fertilizer. The foliar application of Boron was applied at four concentrations which were 0, 100, 150 and 200 mg (H3Bo3). L-1. The results Revealed a significant incr
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ABSTRACT
The study aimed to evaluate the information label of some local pickle products and estimate sodium benzoate therein. 85 samples of locally made pickles were collected from Baghdad city markets and randomly from five different areas in Baghdad it included (Al-Shula, Al-Bayaa, Al-Nahrawan, Al-Taji, and Abu Ghraib), which were divided into groups P1, P2, P3, P4 and P5, respectively, according to those areas, samples information label was scanned and compared with the Iraqi standard specification for the information card of packaged and canned food IQS 230, the results showed that 25.9% of the samples were devoid of the indication card informa
... Show MoreDeep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod
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In this study, phosphorescence analysis (KPA) is used for determining soil collected from the Tigris River from Al- Karrada and Bab Al-Sharq in Baghdad and samples were taken from rainwater collected from Al-Rashad, Al-Obeidi, Al-Dora and Al-Sadr City in Baghdad. The measurements were carried out by the Iraqi Ministry of Health and Environment, in the Radiation Protection Center. The collection, removal and evaporation of the samples ranged from January to the end of March 2018. The results show the presents of concentration of 238U and 235U in soil samples and the rainwater samples. The conclusion of this work is the concentration of uranium in soil samples is more than recommendations by ICRP value of 1.9 μg /l. While all water sample
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