Single-phase thyristor full converter and semi been made in the present paper and all the possible modes of converter fed dc drives with phase angle control scheme have operation and the sub-cases therein have been identified and been analysed under all possible modes of operation of continuous the corresponding expressions for the various performance and discontinuous armature current conduction. For each case, factors derived for both thyristor full converter and semi expressions for the various performance quantities have been v presented comprehensively. All the cases have been simulated converter. using power system block set tool box in MATLAB and the III. MODES OF OPERATION simulation results are found to be same as those obtained from the derived expressions. Results obtained on a 220 V, 5 hp separatelyIn the analysis of these converters, we have duty interval(s) excited dc motor operating under all modes of operation, were in which the ac source is connected to the load through a set of also in agreement with simulated / calculated values. thyristors. Depending on the armature circuit inductance, armature current and speed setting there may be a zero current I. NOMENCLATURE interval also. In addition, in the semi converter there could be a free-wheeling interval in which load current flows only E motor back emf, V through the devices and the source current is zero. Based on io : instantaneous armature current ( load current ), A these, the various modes identified are classified in Table 1. is : instantaneous ac supply current, A fundamental component of rms ac supply current, A I : rms ac supply current, A In any mode of operation, considering one-half cycle i.e., Kb back emf constant, volts/rad/sec starting from the instant of firing of one set of devices to the La motor armature circuit inductance, H instant of firing of the other set of devices, the input and output Ra motor armature circuit resistance, Q voltage and current equations of the converter circuits are: Vm maximum value of the ac supply voltage, V VS : instantaneous ac supply voltage, V Duty interval (is = io) vO : instantaneous converter dc output voltage, V VO = vs = Vm Sin o)t (1) V0 average converter dc output voltage, V Raio +La(dio/dt)+E Vs (2) Z motor impedance, Q oc : firing angle, deg Free-wheeling interval (is = 0): P : extinction angle, deg vO= 0 (3) O instant at which the instantaneous supply voltage is Ra io + La (dio / dt) + E = 0 (4) equal to the motor back emf, deg X : angle at which conduction starts, deg Zero current interval (is = = 0) 0) :rotor speed in rad/sec vO = E (5)

The evolving new and modern technologies raise the risks in the network which will be affected by several attacks and thus give rise to developing efficient network attack detection and classification methods. Here in this article for predicting and classifying the network attacks, the LSTM neural network with XGBoost is suggested in which the NSL-KDD dataset was utilized to train the LSTM in the study. In the beginning, the unnecessary data and the noisy data will be eliminated using the dataset and the feature subset with the most compelling features will be selected using the feature selection. By utilizing the essential data, the proposed system will be trained and the training parameter values will be modified for maximizing the functionality of the proposed system. Then, the result of the proposed system will be evaluated with some of the existing machine learning and deep learning algorithms such as SVM, LR, RF, DNN, and CNN with the performance metrics like Accuracy, F1 score, Recall, and Precision. It was found that the proposed model outperforms better than the other algorithms as this model is trained with the most important features and due to this, the training time and overfitting of the learning model was reduced thereby increasing the model effectiveness

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One main psychiatric disorder found in humans is ASD (Autistic Spectrum Disorder). The disease manifests in a mental disorder that restricts humans from communications, language, speech in terms of their individual abilities. Even though its cure is complex and literally impossible, its early detection is required for mitigating its intensity. ASD does not have a pre-defined age for affecting humans. A system for effectively predicting ASD based on MLTs (Machine Learning Techniques) is proposed in this work. Hybrid APMs (Autism Prediction Models) combining multiple techniques like RF (Random Forest), CART (Classification and Regression Trees), RF-ID3 (RF-Iterative Dichotomiser 3) perform well, but face issues in memory usage, execution times and inadequate feature selections. Taking these issues into account, this work overcomes these hurdles in this proposed work with a hybrid technique that combines MCSO (Modified Chicken Swarm Optimization) and PDCNN (Polynomial Distribution based Convolution Neural Network) algorithms for its objective. The proposed scheme’s experimental results prove its higher levels of accuracy, precision, sensitivity, specificity, FPRs (False Positive Rates) and lowered time complexity when compared to other methods.

Many industrial chemical processes involve exothermic (heat generating) reactions whose temperature needs to be controlled for safe operation of the process. If there is any irregularity in coolant flow, the reactant temperature becomes difficult to control. Reaction continues under unsafe condition till all the reactants finish which may lead to hazardous reaction. Dicumyl peroxide reaction is one such process, where the amount of heat generated is high, which may cause thermal explosion and runaway reaction to occur when the heat liberated is not compensated by the jacket coolant temperature or due to contaminants present in the reactor. The present work comprises identification of the process dynamics by nonlinear ARX model and estimation of internal states of the process reaction using estimators like Kalman Filter (KF), Extended Kalman Filter (EKF) and Unscented Kalman Filters (UKF). Results of these estimators are compared and suitable estimator which provides least mismatch between plant and model will be applied to design the control law for efficient control.