Magneto-rheological (MR) valve is one of the devices generally used to control the speed of Hydraulic actuator of MR fluid. The performance of valve depends on the magnetic circuit design. Present study deals with a new design of MR valve. A mathematical model for the MR valve is developed and the simulation is carried out to evaluate the newly developed MR valve. The design of the magnetic circuit is accomplished by magnetic finite element software such as Finite Element Method Magnetic (FEMMR). The model dimensions of MR valve, material properties are taken into account. The results of analysis are presented in terms of magnetic strength H and magnetic flux density B. The simulation results based on the proposed model indicate that the ef

A load-shedding controller suitable for small to medium size loads is designed and implemented based on preprogrammed priorities and power consumption for individual loads. The main controller decides if a particular load can be switched ON or not according to the amount of available power generation, load consumption and loads priorities. When themaximum allowed power consumption is reached and the user want to deliver power to additional load, the controller will decide if this particular load should be denied receiving power if its priority is low. Otherwise, it can be granted to receive power if its priority is high and in this case lower priority loads are automatically switched OFF in order not to overload the power generation. The

This research has presented a solution to the problem faced by alloys: the corrosion problem, by reducing corrosion and enhancing protection by using an inhibitor (Schiff base). The inhibitor (Schiff base) was synthesized by reacting of the substrates materials (4-dimethylaminobenzaldehyde and 4-aminoantipyrine). It was diagnosed by infrared technology IR, where the IR spectrum and through the visible beams proved that the Schiff base was well formed and with high purity. The corrosion behavior of carbon steel and stainless steel in a saline medium (artificial seawater 3.5%NaCl) before and after using the inhibitor at four temperatures: 20, 30, 40, and 50 C° was studied by using thr

This research has presented a solution to the problem faced by alloys: the corrosion problem, by reducing corrosion and enhancing protection by using an inhibitor (Schiff base). The inhibitor (Schiff base) was synthesized by reacting of the substrates materials (4-dimethylaminobenzaldehyde and 4-aminoantipyrine). It was diagnosed by infrared technology IR, where the IR spectrum and through the visible beams proved that the Schiff base was well formed and with high purity. The corrosion behavior of carbon steel and stainless steel in a saline medium (artificial seawater 3.5%NaCl) before and after using the inhibitor at four temperatures: 20, 30, 40, and 50 C° was studied by using three electrodes potentiostat. The corrosion behavior

This research has presented a solution to the problem faced by alloys: the corrosion problem, by reducing corrosion and enhancing protection by using an inhibitor (Schiff base). The inhibitor (Schiff base) was synthesized by reacting of the substrates materials (4-dimethylaminobenzaldehyde and 4-aminoantipyrine). It was diagnosed by infrared technology IR, where the IR spectrum and through the visible beams proved that the Schiff base was well formed and with high purity. The corrosion behavior of carbon steel and stainless steel in a saline medium (artificial seawater 3.5%NaCl) before and after using the inhibitor at four temperatures: 20, 30, 40, and 50 C° was studied by using three electrodes potentiostat. The corrosion behavior was

Data compression offers an attractive approach to reducing communication costs using available bandwidth effectively. It makes sense to pursue research on developing algorithms that can most effectively use available network. It is also important to consider the security aspect of the data being transmitted is vulnerable to attacks. The basic aim of this work is to develop a module for combining the operation of compression and encryption on the same set of data to perform these two operations simultaneously. This is achieved through embedding encryption into compression algorithms since both cryptographic ciphers and entropy coders bear certain resemblance in the sense of secrecy. First in the secure compression module, the given text is p

In this study, we review the ARIMA (p, d, q), the EWMA and the DLM (dynamic linear moodelling) procedures in brief in order to accomdate the ac(autocorrelation)  structure of data .We consider the recursive estimation and prediction algorithms based on Bayes and KF (Kalman filtering) techniques for correlated observations.We investigate the effect on the MSE of  these procedures and compare them using generated data.

Activity recognition (AR) is a new interesting and challenging research area with many applications (e.g. healthcare, security, and event detection). Basically, activity recognition (e.g. identifying user’s physical activity) is more likely to be considered as a classification problem. In this paper, a combination of 7 classification methods is employed and experimented on accelerometer data collected via smartphones, and compared for best performance. The dataset is collected from 59 individuals who performed 6 different activities (i.e. walk, jog, sit, stand, upstairs, and downstairs). The total number of dataset instances is 5418 with 46 labeled features. The results show that the proposed method of ensemble boost-based classif

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a

Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi