A large number of methodologies for Research and Development (R&D) project evaluation have been developed and reported in the literature over the last few decades. Almost all of this literature focused on the mechanisms and underlying theoretical foundation on which the evaluation methods are based. Very little attention has been paid to the effectiveness and suitability of the techniques, especially a comparison of the techniques. This paper presents a comparative study on a number of classes of R&D evaluation methods based on the Analytic Hierarchy Process. We look into the various factors and characteristics of R&D evaluation methods that affect their suitability. A sensitivity analysis was also carried out to determine the critical factors.

No abstract

No abstract

No abstract

No abstract

Human muscle force estimation is important in biomechanics studies, sports and assistive devices fields. Therefore, it is essential to develop an efficient algorithm to estimate force exerted by muscles. The purpose of this study is to predict force/torque exerted by muscles under dynamic muscle contractions based on continuous wavelet transform (CWT) and artificial neural networks (ANN) approaches. Mean frequency (MF) of the surface electromyography (EMG) signals power spectrum was calculated from CWT. ANN models were trained to derive the MF-force relationships from the subset of EMG signals and the measured forces. Then we use the networks to predict the individual muscle forces for different muscle groups. Fourteen healthy subjects (10 males and 4 females) were voluntarily recruited in this study. EMG signals were collected from the biceps brachii, triceps, hamstring and quadriceps femoris muscles to evaluate the proposed method. Root mean square errors (RMSE) and correlation coefficients between the predicted forces and measured actual forces were calculated.

No abstract

No abstract