Development of Finger ContracturePrevention System For Early Post StrokeRehabilitation

芝浦尾工業大学2016年

A Prediction Model for Natural Frequencies on Kevlar/Glass Hybrid Laminated Composite using Artificial Neural Networks (ANN)

This paper aims to develop a prediction model for the natural frequencies on Kevlar/Glass hybrid laminated composite plates using Artificial Neural Networks (ANN). Finite element simulations were performed to generate data for the natural frequencies under various lamination schemes and fibre angles. Rectangular symmetric and anti-symmetric hybrid laminated composite plates were modeled using commercial software, ANSYS, and meshed using shell elements. The Matlab-ANN tool was used to generate the prediction model, where the generated data (natural frequencies) from the finite element simulations were used for training and testing of the prediction model. The network adapted a two-layer feed-forward algorithm. The adequacy of using ANN in predicting natural frequencies was verified, where the coefficient of determination, R2, was found to be over 0.995. The overall results proved that ANN could be a useful tool, where the prediction model produced an error of less than 5%, when compared to the simulated values of natural frequency of various hybrid laminated composites using finite element analysis. These findings concluded that the current study had contributed significant knowledge in understanding the prediction of natural frequency on hybrid laminated composite using the ANN model

A Prediction Model for Natural Frequencies on Kevlar/Glass Hybrid Laminated Composite using Artificial Neural Networks (ANN)

This paper aims to develop a prediction model for the natural frequencies on Kevlar/Glass hybrid laminated composite plates using Artificial Neural Networks (ANN). Finite element simulations were performed to generate data for the natural frequencies under various lamination schemes and fibre angles. Rectangular symmetric and anti-symmetric hybrid laminated composite plates were modeled using commercial software, ANSYS, and meshed using shell elements. The Matlab-ANN tool was used to generate the prediction model, where the generated data (natural frequencies) from the finite element simulations were used for training and testing of the prediction model. The network adapted a two-layer feed-forward algorithm. The adequacy of using ANN in predicting natural frequencies was verified, where the coefficient of determination, R2, was found to be over 0.995. The overall results proved that ANN could be a useful tool, where the prediction model produced an error of less than 5%, when compared to the simulated values of natural frequency of various hybrid laminated composites using finite element analysis. These findings concluded that the current study had contributed significant knowledge in understanding the prediction of natural frequency on hybrid laminated composite using the ANN model

A Parametric Investigation on the Neo-Hookean Material Constant

This paper assesses the Neo-Hookean material parameters pertaining to deformation behaviour of hyperelastic material by means of numerical analysis. A mathematical model relating stress and stretch is derived based on Neo-Hookeans strain energy function to evaluate the contribution of the material constant, C1, in the constitutive equation by varying its value. A systematic parametric study was constructed and for that purpose, a Matlab programme was developed for execution. The results show that the parameter (C1) is significant in describing material properties behaviour. The results and findings of the current study further enhances the understanding of Neo-Hookean model and hyperelastic materials behaviour. The ultimate future aim of this study is to come up with an alternative constitutive equation that may describe skin behaviour accurately. This study is novel as no similar parametric study on Neo-Hookean model has been reported before

Development of a quantitative evaluation method in occupational therapy exercise for upper limb motor function rehabilitation / Hokyoo Lee ...[et al.]

Rehabilitation exercises required to sustain a motivation amongst patients and demands a quantitative in rehabilitation field. In this study, we developed a measuring equipment for upper limb motor function rehabilitation using optical sensor to improvise these problems. This system consisted of an optical sensor device, a personal computer and automatic calculated software for upper limb position. The measuring equipment was designed to measure the optical sensor position and movement length during the task of sanding movement and wiping movement. The movement positions were calculated based on the motion captured by optical sensors. We found that the accuracy of the optical sensor trajectories was similar in all actual measured value. These results are proposed to be very beneficial in the development of rehabilitation training programs and evaluation methods for patients who needs upper limb motor functions

Innovative development of a flying robot with a flexible manipulator for aerial manipulations

This paper presents an innovative development of a flying robot or an aerial robot, with a flexible manipulator, called the Dexterous Aerial Robotic System (DFTS), for aerial manipulations, especially for inspections and reparations of various structures such as wind turbines, power lines and open gas pipelines, decorations and painting of high industrial chimneys and walls of high buildings, as well as transport and delivery of courier shipments, relocation and manipulation of assemblies and units in inaccessible or dangerous environments. The proposed DFTS consists of two independent but interconnected systems or functional units, which have two main separate functions respectively, including a basic carrying function, and a precise positioning and stabilization function. The system with a basic carrying function is actually the main flying system, the un-manned aerial vehicle (UAV); it is remotely controlled and piloted. Meanwhile, the aerial manipulation platform, called the vertical take-off and landing platform VTOL, which is an active flying platform with 6 degrees of freedom (DOF) is used for positioning and stabilization; and it is attached to the UAV via the soft link. With the use of a long soft link, the problems which are caused by the air turbulent flows generated by the UAV are minimized, and the aerial manipulations of objects are safely controlled and operated. The VTOL which is equipped with a grasping mechanism was successfully developed, prototyped and tested. The experimental results showed that, the developed VTOL can self-stabilize with the inclination angle of being up to 8 degrees

Development of a smart system for early detection of forest fires based on unmanned aerial vehicles

The naturally occurring wildfires and the people-related forest fires are events, which in many cases have significant impact on the environment, the wildlife and the human population. The most devastating among these events usually start in unpopulated remote areas, which are difficult to inspect or are not constantly being monitored or observed. This gives the local small-sized fires enough time to evolve into full-scale wide-area disasters, which in turn makes their suppression and extinguishing very difficult. In this paper, we present an autonomous system for early detection of forest fires, named THEASIS-M. The presented system represents a solution that is based on a combination of innovative technologies, including computer vision algorithms, artificial intelligence and unmanned aerial vehicles. In the first part of the study, we provide an overview on the present applications of the UAVs in the forestry domain. The paper then introduces the general architecture of the THEASIS-M system and its components. The system itself is fully autonomous and is based on several different types of UAVs, including a fixed-wing drone, which provides the overall forest monitoring capabilities of the proposed solution, and a rotary-wing UAV that is used for confirmation and monitoring of the detected fire event. The widely used technologies for computer vision and image processing, which are used for the detection of fire and smoke in the real-time video streams sent from the UAVs to the ground control station, are highlighted in the next section of this study. Finally, the experimental tests and demonstrations of the proposed THEASIS-M system are presented and briefly discussed

System Integration and Control of Dynamic Ankle Foot Orthosis for Lower Limb Rehabilitation

Gait disorder is the inability of a person to assume upright position, maintain neither balance nor the aptitude to initiate and sustain rhythmic stepping. This form of disability may originate from cerebellar disease, stroke, spinal injury, cardiac disease or other general conditions that may bring about such disorder. Studies have shown that one's mobility may be improved with continuous locomotor activity. Traditional rehabilitation therapy is deemed labour as well as cost intensive. Rehabilitation robotics has been explored to address the drawbacks of conventional rehabilitation therapy and the increasing demand for gait rehabilitation. This paper presents a simple yet decent technique in the control and actuation of a new Dynamic Ankle-Foot Orthosis (DAFO) designed to rehabilitate the dorsiflexion and plantarflexion motion of the ankle. The DAFO is equipped with two force sensitive resistors (FSR), which act as a limit switch controlling the actuation of the DC motor to a certain dorsiflexion/plantarflexion motion according to the gait phases detected. The results show that the two FSR sensors are sufficient to detect gait phases and act as limit switches to control the actuation of the ankle DC motors, and thus proving the potential of the current system and design for future application

Quantifying and Predicting the Tensile Properties of Silicone Reinforced with Moringa oleifera Bark Fibers

To obtain a better understanding of using Moringa oleifera bark (MOB) as a reinforcement in a silicone matrix, this study aimed to define the mechanical properties of this new material under uniaxial tension. Composite samples of 0 wt%, 4 wt%, 8 wt%, 12 wt%, and 16 wt% MOB powder were produced. The tensile properties were quantified mathematically using the neo-Hookean hyperelastic model. The collected data were employed to establish multiple inputs of an artificial neural network (ANN) to predict its material constant via MATLAB. The result showed that the material constant for the 16 wt% fiber content sample was 63.9% higher than pure silicone. This was supported by the tensile modulus testing, which indicated that the modulus increased as the fiber content increased. However, the elongation ratio (λ) of the MOB-silicone biocomposite decreased slightly compared to the pure silicone. Lastly, the prediction of the material constant using an ANN recorded a 2.03% percentage error, which showed that it was comparable to the mathematical modelling. Therefore, the inclusion of MOB fibers into silicone produced a stiffer material and gradually improved the composite. Furthermore, the network that had multiple inputs (weighting, load, and elongation) was more reliable to produce precise predictions