Robert C. Creese

Assessment of neck and shoulder muscle fatigue using discrete wavelet transforms of surface electromyography

Objective assessment of neuromuscular fatigue is important for the early detection and prevention of risks of work-related musculoskeletal disorders (MSDs). Although, in recent years, a number of researchers have used discrete wavelet transforms (DWT) of surface electromyography (SEMG) to evaluate muscle fatigue, its application to neck and shoulder muscle fatigue assessment is not well established. Therefore, the purpose of this study was to establish DWT analysis as a suitable method to conduct quantitative assessment of neck and shoulder muscle fatigue caused by dynamic exertions. Six human participants performed 40 minutes of fatiguing repetitive arm and neck exertions. SEMG data from the right upper trapezius and left sternocleidomastoid muscles were recorded. Six most commonly used orthogonal wavelet functions were used to conduct DWT analysis. With the development of fatigue for most of the wavelet functions a significant increase in the power in the lower frequency bands of 12-23Hz and 23-46Hz was observed. Bior 3.1 wavelet showed the highest power, statistically consistent and meaningful power trend and the highest overall power contrast compared to the remaining five wavelets tested in this study.

Genetic Algorithm for Neural Networks optimization

This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB®.

Return of feature-based cost modeling

Feature Based Cost Modeling is thought of as a relative new approach to cost modeling, but feature based cost modeling had considerable development in the 1950s. Considerable work was published in the 1950s by Boeing on cost for various casting processes--sand casting, die casting, investment casting and permanent mold casting--as a function of a single casting feature, casting volume. Additional approaches to feature based cost modeling have been made, and this work is a review of previous works and a proposed integrated model to feature based cost modeling.

Improvement Curves in Manufacturing

This study grew out of the importance of cost modeling to concurrent engineering (cooperative product development) and has resulted in the development of general forms of models for predicting the average unit cost, unit cost and cumulative cost of products. The cost predicting equations were derived from the unit curve model, which assumes that the unit time versus the production units generate a straight line on logarithmic paper. The unique contribution here is that the effect of multiple improvement curves on time saving were studied and compared with improvement curves with single improvement rate and curves with no improvement. This is applicable for both decreasing and increasing improvement rates as well as considering multiple improvement curves. Another important application of the improvement curve is to predict the loss of improvement during manufacturing interruption when long time periods of no production occur. These new cost prediction tools were introduced to help cost analysts practice concurrent engineering in a general manufacturing environment. A comparison of the differences between the unit cost model and average unit cost model will be made.