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Miniature size internal combustion (IC) engines with displacements in the neighborhood of 1 cc have the potential to function as high power and energy density miniature power supplies. The engines, which are currently used in model airplanes, have higher power and longer run times than a battery coupled to an electric motor. The Center for Compact and Efficient Fluid Power (CCEFP), a seven university research consortium in the United States, is developing miniature IC engines for applications in human size mobile devices such as robots and medical assistive devices, aiming at more compact design and longer operation time. To advance the development of new compact power supplies, it is necessary to understand the performance characteristics of current miniature engines. As the scale of an engine gets smaller, surface effects such as friction, heat loss, and leakage get more significant [ 1 ].

A miniature homogenous charge compression ignition (HCCI) free-piston engine compressor aimed at an ankle-foot orthosis application is described. Analysis of the human ankle shows that a fluid power source in the neighborhood of 10 W is needed. To account for compressor and actuator inefficiencies, the power output at the engine cylinder is designed to be 30 W. A compact engine compressor package has been designed and mathematically modeled. Experiments using existing engine components characterized the leakage model. Through the dynamic simulation of the engine, major parameters of the engine have been specified. Simulations indicate that the HCCI free-piston engine compressor, designed in a prototype package scale of about 80×40×20 mm is a viable compact and efficient fluid power supply. Simulation results demonstrate that the overall efficiency of the engine compressor is expected to be 5.9% and that the package should have a higher energy density than batteries.

Yield potential was predicted and mapped for three corn fields in Central Illinois, using digital aerial color infrared images. Three methods, namely statistical (regression) modeling, genetic algorithm optimization and artificial neural networks, were used for developing yield models. Two image resolutions of 3 and 6 m/pixel were used for modeling. All the models were trained using July 31 image and tested using images from July 2 and August 31, all from 1998. Among the three models, artificial neural networks gave best performance, with a prediction error less than 30%. The statistical model resulted in prediction errors in the range of 23 to 54%. The lower resolution images resulted in better prediction accuracy compared to resolutions higher than or equal to the yield resolution. Images after pollination resulted in better accuracy compared to images before pollination.