This paper presents a speed control of a separately excited DC motor by using PI (Proportional Integral). The speed of the separately excited DC motor can be varied below and above the rated speed by various speed control techniques. It can be varied above the rated speed by field flux control and below the rated speed by armature terminal voltage control. The conventional controllers are commonly being used to control the speed of the DC motors in various industrial applications. It's found to be simple, robust and highly effective, when the load disturbance is small. Here, we using chopper as a converter the speed of DC motor is controllable. The chopper firing circuit gets signal from controller and then by supplying variable voltage to the armature of the motor then to obtain the desired speed of the motor. There are two different types of control loops, current controller and speed controller. The controller used is ProportionalIntegral type. The current and speed controller loop is designed and in order to get stable and high speed control of DC motor. The simulation of the above model is done in MATLAB/SIMULING under varying speed and torque condition.
This paper presents a photovoltaic (PV) system to convert the solar energy into electrical energy. DC power from PV system is converted into AC power using multilevel inverters. Cascaded H-bridge (CHB) inverter and diode clamped inverter (DCI) are used to convert variable DC power into sinusoidal AC power. Harmonic content is the important part to improve the efficiency of the inverter. Harmonics of CHB inverter and DCI are simulated and analyzed with different pulse width modulation (PWM) techniques.
This paper presents a fault protection of three phase induction motor. Induction motor is common in many industries. Fault can be occurred due to over speed, over load, high temperature, vibration, over current and over voltage. Complete monitoring is necessary to prevent the motor from the damage. Various protection schemes are implemented to protect the motor from the fault. Here we come with the PLC (Programmable Logic Controller) and SCADA (Supervisory Control and Data Acquisition) for the detection and protection of three phase induction motor from the fault. PLC is used to control the system and the SCADA is used to control and monitor the motor. Motor status can be viewed and controlled in remote location by using the SCADA software. If any fault occurs, motor automatically turns off and gives the warning to the person working in the vicinity. Hereby we can avoid unexpected failure of the motor and prevent the whole industrial process from shutting down all of a sudden which can be dangerous to the people working in the industry. This possesses high efficiency, low cost and more safety benefits than relay basedsystem.
Facial Expression Recognition (FER) has been an interesting area of research in places where there is human-computer interaction. Human psychology, emotions and behaviors can be analyzed in FER. Classifiers used in FER have been perfect on normal faces but have been found to be constrained in occluded faces. Recently, Deep Learning Techniques (DLT) have gained popularity in applications of real-world problems including recognition of human emotions. The human face reflects emotional states and human intentions. An expression is the most natural and powerful way of communicating non-verbally. Systems which form communications between the two are termed Human Machine Interaction (HMI) systems. FER can improve HMI systems as human expressions convey useful information to an observer. This paper proposes a FER scheme called EECNN (Enhanced Convolution Neural Network with Attention mechanism) to recognize seven types of human emotions with satisfying results in its experiments. Proposed EECNN achieved 89.8% accuracy in classifying the images.
Skin cancer is very important notable disease and it is probable to everyone nowadays, it flourishes on the area of body where it exposed to ultraviolet rays. It leads anomalous gain in skin cells. It initiate on various parts of body like face, hand and bottoms of the feet as cautious hole or spot. The initial investigation of anomalous gain is essence to cure the disease at early stage, and it still remains a feasible challenge in the scientific improvements. From the analysis, this paper endeavour to inspect the category of disease with the following improvements. Initially, the skin dataset from ISIC machine archive is utilized for image processing. Secondly, the values of dataset images are normalized by dividing all the RGB values by 255. Thirdly, keras sequential API is used to add one layer at a time, initiating from the input. The CNN can extract the features that are useful for classifying the image, by using the kernel filter matrix. MaxPool reduce the computational cost by down-sampling the image, and the relu activation function is implemented to provide non linearity to the network. The flatten layer is utilized to remodel the final feature maps into 1D vector. CNN model provides accuracy of 94.83% with 3297 images and ResNet 50 model has attained accuracy of 90.78% due to less number of images used for classification. AlexNet model has attained accuracy of 81.8% with 1300 images and GoogleNet V3 inception has attained accuracy of 96% with 3374 images. Finally Vgg16 model has attained accuracy of 97.3% with 5636 samples.
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