Modified Predictive Control for a Class of Electro-Hydraulic Actuator

Many model predictive control (MPC) algorithms have been proposed in the literature depending on the conditionality of the system matrix and the tuning control parameters. A modified predictive control method is proposed in this paper. The modified predictive method is based on the control matrix formulation combined with optimized move suppression coefficient. Poor dynamics and high nonlinearities are parts of the difficulties in the control of the Electro-Hydraulic Actuator (EHA) functions, which make the proposed matrix an attractive solution. The developed controller is designed based on simulation model of a position control EHA to reduce the overshoot of the system and to achieve better and smoother tracking. The performance of the designed controller achieved quick response and accurate behavior of the tracking compared to the previous study

A conceptual framework for predicting the effects of encroachment on magnitude of flood in Foma-river area, Kwara State, Nigeria using data mining

Flooding occurs but there is no flood hazard. It is only after human encroachment into the floodplain that turn into hazard. The practice of continuous increase of properties development along the floodplain and indiscriminate refuse disposal into water channels have been a major constituting factors to intensive flooding along the floodplain. This is as a result of decline in the capacity of floodplain to absorbs excess flooding, thus resulting to exposing more urban areas to be vulnerable to flood. Foma-river is located in Ilorin Kwara, Nigeria on latitude N08,49574 and longitude E004,5107. Climate of Ilorin comprises of the dry and wet seasons with the wet season starting around March and lasting for about four to five months. This study intends to propose a conceptual framework to support the prediction of effects of season on magnitude of flood in Foma-river area using data mining approach based on 7 years sampled data from Nigeria Meteorological Agency (NIMET), and questionnaire responses from residents along Foma-river floodplai

An updated checklist of the herpetofauna of the Belum-Temengor forest reserves, Hulu Perak, Peninsular Malaysia

A herpetofaunal survey was carried out in Temengor Forest Reserve, Peninsular Malaysia during the second Temengor Scientific Expedition conducted from 1st to 10th October 2012. This study represents the first records of amphibians and reptiles of Sungai Enam Basin and an updated record of Belum-Temengor Forest Reserve. In this survey, a total of 27 species of herpetofauna was recorded comprising 12 species of amphibians from four families and 15 species of reptiles from six families. No new records of frog or lizard species are reported in this study. However, three new records of snakes are reported from the area

The Effects of Progesterone on Hypoxic Ischemic Injuries in the Cornu Ammonis (CA) Region of the Hippocampus of Neonatal Rats

Hypoxia-ischemia (HI) is a major cause of brain damage in the newborn. Several studies elicited the neuroprotective effects of progesterone in adult rats but there is very little literature available on neonatal rats. Therefore the present study is undertaken to see the effect of progesterone in hypoxic ischemic brain injury in neonatal rats, using an established neonatal HI rat pup model. Sevenday- old rat pups were subjected to right common carotid artery ligation and then 60 minutes hypoxia. The first dose of progesterone to treatment group was administered by peritoneal injection (4 mg/kg), after 10 minutes of exposure and subsequent doses were given by subcutaneous injection at 6 h, 24 h and 48 h intervals. Control group was also exposed to HI and was given only the vehicle (peanut oil) through the same route and intervals as that of treatment group. After 96 h, the pups were perfused with 10% formalin and brains were sampled and stained with toluidine blue. Cells density and number of pyramidal cells of the hippocampal Cornu Ammonis (CA) regions were examined by stereological methods. The histomorphometric assessment of the effects of progesterone showed minimal but no significant protective value in the volume, cells density and total number of pyramidal cells of hippocampal CA region of the treatment and control groups (p>0.05) after HI. Our results concluded that 4 mg/kg of PROG had no significant neuroprotective effect in HI model of the neonatal rat’s hippocampus

Linear and Non-Linear Predictive Models in Predicting Motor Assessment Scale of Stroke Patients Using Non-Motorized Rehabilitation Device

Various predictive models, both linear and non-linear, such as Multiple Linear Regression (MLR), Partial Least Squares (PLS), and Artificial Neural Network (ANN), were frequently employed for predicting the clinical scores of stroke patients. Nonetheless, the effectiveness of these predictive models is somewhat impacted by how features are selected from the data to serve as inputs for the model. Hence, it's crucial to explore an ideal feature selection method to attain the most accurate prediction performance. This study primarily aims to evaluate the performance of two non-motorized three-degree-of-freedom devices, namely iRest and ReHAD using MLR, PLS and ANN predictive models and to examine the usefulness of including a hand grip function with the assessment device. The results reveal that ReHAD coupled with non-linear model (i.e. ANN) has a better prediction performance compared to iRest and at once proving that by including the hand grip function into the assessment device may increase the prediction accuracy in predicting Motor Assessment Scale (MAS) score of stroke subjects. Furthermore, these findings imply that there is a substantial association between kinematic variables and MAS scores, and as such the ANN model with a feature selection of twelve kinematic variables can predict stroke patients' MAS scores

Linear and Non-Linear Predictive Models in Predicting Motor Assessment Scale of Stroke Patients Using Non-Motorized Rehabilitation Device

Various predictive models, both linear and non-linear, such as Multiple Linear Regression (MLR), Partial Least Squares (PLS), and Artificial Neural Network (ANN), were frequently employed for predicting the clinical scores of stroke patients. Nonetheless, the effectiveness of these predictive models is somewhat impacted by how features are selected from the data to serve as inputs for the model. Hence, it's crucial to explore an ideal feature selection method to attain the most accurate prediction performance. This study primarily aims to evaluate the performance of two non-motorized three-degree-of-freedom devices, namely iRest and ReHAD using MLR, PLS and ANN predictive models and to examine the usefulness of including a hand grip function with the assessment device. The results reveal that ReHAD coupled with non-linear model (i.e. ANN) has a better prediction performance compared to iRest and at once proving that by including the hand grip function into the assessment device may increase the prediction accuracy in predicting Motor Assessment Scale (MAS) score of stroke subjects. Furthermore, these findings imply that there is a substantial association between kinematic variables and MAS scores, and as such the ANN model with a feature selection of twelve kinematic variables can predict stroke patients' MAS scores