Direct torque control of IM using PID controller

Direct torque control "DTC" technique is one of a high performance control system of an AC motor drive, which was proposed after the vector oriented control scheme during the resent 25 years. It has been developed rapidly for its concise system scheme, transient and dynamic performance. The DTC mechanism consists of voltage vector selection table, two hysteresis comparators and two estimators one for stator flux and another for electromagnetic torque. DTC is directly control torque and flux by using Voltage Source Inverter VSI, space vector and stator flux orientation and indirect speed regulated. A several control techniques can be used for improving the torque and flux performance. In this paper, the DTC with Proportional-Integral-Derivative (PID) controller used to improve the starting and dynamic performance of asynchronous motor AM, which gives good torque and flux response, best speed control and also minimize the unacceptable torque ripple. The mathematical model of DTC with PID controller of 3-phase induction motor IM are simulated under Matlab-Simulink. Therefore, the DTC based on PID controller has good performance of IM compared to classical DTC for starting, running state and also during change in load

Comparison of some reliability estimation methods for Laplace distribution using simulations

In this paper, we derived an estimator of reliability function for Laplace distribution with two parameters using Bayes method with square error loss function, Jeffery’s formula and conditional probability random variable of observation. The main objective of this study is to find the efficiency of the derived Bayesian estimator compared to the maximum likelihood of this function and moment method using simulation technique by Monte Carlo method under different Laplace distribution parameters and sample sizes. The consequences have shown that Bayes estimator has been more efficient than the maximum likelihood estimator and moment estimator in all samples sizes

Classification of EEG Signal by Using Optimized Quantum Neural Network

In recent years the algorithms of machine learning was used for brain signals identifing which is a useful technique for diagnosing diseases like Alzheimer's and epilepsy. In this paper, the Electroencephalogram (EEG) signals are classified using an optimized Quantum neural network (QNN) after normalizing these signals, wavelet transform (WT) and the independent component analysis (ICA), were utilized for feature extraction.  These algorithms used to reduces the dimensions of the data, which is an input to the optimized QNN for the purpose of performing the classification process after the feature extraction process. This research uses an optimized QNN, a form of feedforward neural network (FFNN), to recognize (EEG) signals. The Particle swarm optimization (PSO) algorithm was used to optimize the quantum neural network, which improved the training process of the system's performance. The optimized (QNN) provided us with somewhat faster and more realistic results. According to simulation results, the total classification for (ICA) is 82.4 percent, while the total classification for (WT) is 78.43 percent; from these results, using the ICA for feature extraction is better than using WT

Electrocardiograph signal recognition using wavelet transform based on optimized neural network

Due to the growing number of cardiac patients, an automatic detection that detects various heart abnormalities has been developed to relieve and share physicians’ workload. Many of the depolarization of ventricles complex waves (QRS) detection algorithms with multiple properties have recently been presented; nevertheless, real-time implementations in low-cost systems remain a challenge due to limited hardware resources. The proposed algorithm finds a solution for the delay in processing by minimizing the input vector’s dimension and, as a result, the classifier’s complexity. In this paper, the wavelet transform is employed for feature extraction. The optimized neural network is used for classification with 8-classes for the electrocardiogram (ECG) signal this data is taken from two ECG signals (ST-T and MIT-BIH database). The wavelet transform coefficients are used for the artificial neural network’s training process and optimized by using the invasive weed optimization (IWO) algorithm. The suggested system has a sensitivity of over 70%, a specificity of over 94%, a positive predictive of over 65%, a negative predictive of more than 93%, and a classification accuracy of more than 80%. The performance of the classifier improves when the number of neurons in the hidden layer is increased

Performance of Concrete Containing Iron Fillings

تشكل المخلفات الصناعية الغير قابلة للتحلل البيولوجي خطرًا بيئيًا كبيرًا على الكائنات الحية ويتطلب التخلص منها بذل الجهد والوقت والمال. ومن بين العمليات الأكثر فائدة التي يمكن استغلال هذه المخلفات هي عملية إعادة تدويرها واستخدامها في المجالات الهندسية. برادة الحديد هي واحدة من المخلفات التي يمكن إعادة تدويرها واستخدامها في المجالات الهندسية. واحد من الاستخدامات هو استخدامها في عملية البناء. برادة الحديد هي قطع صغيرة من الحديد التي تبدو وكأنها مسحوق ناعم. وغالبا ماتستخدم في الاثباتات والبراهين العلمية لاضهار اتجاه المجال المغناطيسي. والغرض من هذا المشروع هو تقييم إمكانية استخدام برادة الحديد كأحد مكونات الخرسانة. تمت إضافة ثلاث نسب مؤية مختلفة من برادة الحديد إلى خليط الخرسانة لقياس التباين الذي يمكن ان يحدث في مقاومة الانضغاط والشد بعد 28 يومًا. تم اجراء واختبار 45 مكعب بأبعاد 150*150*150 مم والعتبات الموشورية بأبعاد 100*100* 400مم في هذه الدراسة باستخدام 0 ٪ (السيطرة)، 5 ٪، 10 ٪ و15 ٪ من برادة الحديد في خليط الخرسانة. Non-biodegradable wastes materials pose a significant environmental hazard to living organisms and their disposal requires effort, time and money. One of the most useful processes by which this waste can be exploited is the recycling process. Iron filings are one of the wastes materials that can be recycled and used in engineering fields. One of these is the use of the process in construction. Iron filings are very small pieces of iron that look like a light powder. They are very often used in science demonstrations to show the direction of a magnetic field. The purpose of this project is to evaluate the possibility of using iron filings as one of the component of concrete mix. Three different percentage of iron filings were added to concrete mix to measure the variation, which may be obtained in compression and tensile concrete strengths after 28 days. 45 of 150mm cubes and prisms of 100x100x400mm were performed and tested in this study using 0% (control), 5%, 10% and 15% of iron filing in concrete mix

Sensored speed control of brushless DC motor based salp swarm algorithm

This article uses one of the newest and efficient meta-heuristic optimization algorithms inspired from nature called salp swarm algorithm (SSA). It imitates the exploring and foraging behavior of salps in oceans. SSA is proposed for parameters tuning of speed controller in brushless DC (BLDC) motor to achieve the best performance. The suggested work modeling and control scheme is done using MATLAB/Simulink and coding environments. In this work, a 6-step inverter is feeding a BLDC motor with a Hall sensor effect. The proposed technique is compared with other nature-inspired techniques such as cuckoo search optimizer (CSO), honey bee optimization (HBO), and flower pollination algorithm (FPA) under the same operating conditions. This comparison aims to show the superiority features of the proposed tuning technique versus other optimization strategies. The proposed tuning technique shows superior optimization features versus other bio-inspired tuning methods that are used in this work. It improves the controller performance of BLDC motor. It refining the speed response features which results in decreasing the rising time, steady-state error, peak overshoot, and settling time

New data on Gnaphosidae (Arachnida, Araneae) of Iraq

New faunistic data are provided on the ground spiders (Araneae: Gnaphosidae) of Iraq. Three genera (Haplodrassus Chamberlin, 1922; Minosiella Dalmas, 1921; Odontodrassus Jézéquel, 1965) and six species (Haplodrassus dalmatensis (L. Koch, 1866); Minosiella intermedia Denis, 1958; Odontodrassus aravaensis Levy, 1999; Odontodrassus mundulus (O. Pickard-Cambridge, 1872); Pterotricha dalmasi Fage, 1929; Zelotes fagei Denis, 1955) are reported in Iraq for the first time, and the previously unknown female of Pterotricha kovblyuki Zamani & Marusik, 2018 is described. In addition, a list of all gnaphosids reported from Iraq (16 spp.) is provided. </p

New data on Gnaphosidae (Arachnida, Araneae) of Iraq

New faunistic data are provided on the ground spiders (Araneae: Gnaphosidae) of Iraq. Three genera (Haplodrassus Chamberlin, 1922; Minosiella Dalmas, 1921; Odontodrassus Jézéquel, 1965) and six species (Haplodrassus dalmatensis (L. Koch, 1866); Minosiella intermedia Denis, 1958; Odontodrassus aravaensis Levy, 1999; Odontodrassus mundulus (O. Pickard-Cambridge, 1872); Pterotricha dalmasi Fage, 1929; Zelotes fagei Denis, 1955) are reported in Iraq for the first time, and the previously unknown female of Pterotricha kovblyuki Zamani &amp;amp; Marusik, 2018 is described. In addition, a list of all gnaphosids reported from Iraq (16 spp.) is provided

Development and application of energy-efficient medical beds based on IoT for patient monitoring

Later logical accomplishments and mechanical progresses have brought forward a gigantic show of modern or overhauled restorative gadgets, empowered with highly evolved embedded-control capacities and interactivity. From the ultimate decade of the 20th century, restorative beds have specifically been motivated through this surge, taking on unused shapes and capacities, whereas obliging to installation properties that have gotten to be famous for those gadgets. The beyond fifteen a long time have too delivered ahead modifications to conceptual systems, concerning the item plan and fabricating forms, As properly as the persistent viewpoints on patient-care situations and openness. This paper offers the components and the steps of design and implementation of electric medical bed supplied with monitoring devices and various sensors working together to give the Arduino a full report about the conditions of patient and according to this report the right decisions can be taken. The principles of internet of things (IOT) are applied to achieve this instrument. The paper primarily focuses on the objectives, components, and functionality of the system, such as monitoring physical parameters, detecting emergencies, reducing workload, and communicating with caretakers

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. Methods: The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. Findings: Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). Interpretation: Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions