A computing task ergonomic risk assessment tool for assessing risk factors of work related musculoskeletal disorders

Observation method remains to be the most widely applied method in assessing exposure to risk factors for work-related musculoskeletal disorders (WMSDs) related to office works because it is inexpensive and applicable to wide range of office jobs. However, the existing research that applied this method was mainly focused to a limited range of office components and computer accessories such as seat pan, keyboards, mouse, monitor and telephone. In addition, further testing of reliability and validity of the observational method was less reported. This study was conducted to propose the new office ergonomic risk assessment (OFFERA) method to assess a wide range of office risk factors related to WMSDs, which include office components and office environment where this method covers both right and left side of the body part. The initial development of OFFERA method was divided into two stages, the development of OFFERA system components and psychometric properties of OFFERA method. In reliability testing, the results of inter and intra observer reliability recorded good (K=0.62-0.78) and very good (K=0.81-0.96) agreement among the observers. Meanwhile, in validity testing, the relationship of the final score of OFFERA to the musculoskeletal symptoms statistically shows a significant value for wrists/hands (χ²=7.942; p=0.047), lower back (χ²=13.478; p=0.000), knees (χ²=7.001; p=0.008), and ankle/leg (χ²=5.098; p=0.024). The usability testing shows that the OFFERA method was easy and quick to be used (mean 4.48 ± 0.821) and applicable for wide range of office working activities (mean 4.02 ± 0.952). Based on the results obtained, it can be concluded that the OFFERA method was found to be practically reliable and applicable for wide range of office work-related activities

System Identification for Nonlinear Control Using Neural Networks

An approach to incorporating artificial neural networks in nonlinear, adaptive control systems is described. The controller contains three principal elements: a nonlinear inverse dynamic control law whose coefficients depend on a comprehensive model of the plant, a neural network that models system dynamics, and a state estimator whose outputs drive the control law and train the neural network. Attention is focused on the system identification task, which combines an extended Kalman filter with generalized spline function approximation. Continual learning is possible during normal operation, without taking the system off line for specialized training. Nonlinear inverse dynamic control requires smooth derivatives as well as function estimates, imposing stringent goals on the approximating technique

Power system fault prediction using artificial neural networks

The medium term goal of the research reported in this paper was the development of a major in-house suite of strategic computer aided network simulation and decision support tools to improve the management of power systems. This paper describes a preliminary research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. To achieve this goal, an AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system . Simulation will normally take place using equivalent circuit representation. Artificial Neural Networks (ANNs) are used to construct a hierarchical feed-forward structure which is the most important component in the fault detector. Simulation of a transmission line (2-port circuit ) has already been carried out and preliminary results using this system are promising. This approach provided satisfactory results with accuracy of 95% or higher