Enidine Gmbh

Other

Kenichi Tomita

Gerald J Spyche, Jr

Scott Taylor

Benjamin T Houghton

R Leon Clinard

A shock and vibration isolator that includes a double acting mechanical spring assembly that is mounted to act in parallel with a fluid or liquid spring assembly. The mechanical spring is arranged to provide a first spring rate over a first displacement range and a second spring rate over a further second displacement range. The liquid spring is connected to an accumulator by control circuitry that is under the control of a microprocessor to release stored energy which combines with that of the mechanical spring to rapidly dissipate the input G forces to a level sufficient to protect a substructure.

Semi-active isolator

Apparatus is disclosed for protecting a structural object from the potentially harmful effects of a cyclic event such as an earthquake or high wind loads. A fluid spring is employed to stiffen the structural object and absorb kinetic energy stored by the spring. Fluid from the spring is exchanged with an accumulator where kinetic energy is dissipated as heat. A flow circuit regulates the exchange of fluid between the spring chamber and the accumulator chamber under controlled conditions to absorb the harmful effects of high external loads and to release the structural object when a high shock load is experienced.

Device using compressible fluid as switchable fluid spring for shock and vibration isolation and mitigation

A system for building an artificial neural network is provided which precisely defines the network's structure of artificial neurons, and non-iteratively determines the synapse-weights and hard limiter threshold of each artificial neuron of the network. The system includes a computer for analyzing input data, which represents patterns of different classes of signals, to generate one or more data points in two or three dimensions representative of the signals in each of the different classes. A distribution of the data points is visualized on a map on an output device coupled to the computer. The data points are clustered on the map into clusters in accordance with the classes associated with the data points, and the map is then partitioned into regions by defining linear boundaries between clusters. The artificial neural network is configured in accordance with the data points, clusters, boundaries, and regions, such that each boundary represents a different artificial neuron of the artificial neural network, and the geometric relationship of the regions on the map to the classes defines the logic connectivity of the artificial neurons. The synaptic weights and threshold of each artificial neuron in the network are graphically determined based on the data points of the map.

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Williamsville, NY, United States of America

Kenichi Tomita