989 resultados para Bodine Electric Company.


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Mode of access: Internet.

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Ed Tilin a graduate of the New York Trade School's Advanced Television program is pictured here as part of the General Electric Company. Original caption reads, "Ed Tilin - Advanced Television 1954, joined G.E. in 1956 and has risen rapidly. He now supervises all television product service, product training and consumer relations activities for the New York district. He is a member of the exemtive [sic] board of CETA (Certified Electronic Technicians Association). Black and white photograph with original caption glued to reverse.

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Mode of access: Internet.

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Walter D’Arcy Ryan was born in 1870 in Kentville, Nova Scotia. He became the chief of the department of illumination at the General Electric Company of Schenectady, New York. He was a founder in the field of electrical illumination. He built the electric steam scintillator which had numerous nozzles and valves. The operator would release steam through the valves. The nozzles all had names which included: Niagara, fan, snake, plume, column, pinwheel and sunburst. The steam scintillator was combined with projectors, prismatic reflectors, flashers and filters to produce the desired effects. In 1920 a group of businessmen from Niagara Falls, New York formed a group who called themselves the “generators’. They lobbied the American and Canadian governments to improve the illumination of the Falls. They were able to raise $58, 000 for the purchase and installation of 24 arc lights to illuminate the Falls. On February 24th, 1925 the Niagara Falls Illumination Board was formed. Initially, the board had a budget of $28,000 for management, operation and maintenance of the lights. The power was supplied free by the Ontario Power Company. They had 24 lights installed in a row on the Ontario Power Company surge tank which was next to the Refectory in Victoria Park on the Canadian side. The official opening ceremony took place on June 8th, 1925 and included a light parade in Niagara Falls, New York and an international ceremony held in the middle of the Upper Steel Arch Bridge. Walter D’Arcy Ryan was the illuminating engineer and A.D. Dickerson who was his New York field assistant directed the scintillator. with information from American Technological Sublime by David E. Nye and the Niagara Falls info website Location: Brock University Archives Source Information: Subject Headings: Added Entries: 100 Ryan, W. D’A. |q (Walter D’Arcy), |d 1870-1934 610 General Electric Company 650 Lighting, Architectural and decorative 650 Lighting |z New York (State) |z Niagara Falls 700 Dickerson, A.F. 700 Schaffer, J.W. Related material held at other repositories: The Niagara Falls Museum in Niagara Falls, Ontario has a program (pamphlet) dedicating new lighting in 1958 and it has postcards depicting the illumination of the Falls. Some of Ryan’s accomplishments can be seen at The Virtual Museum of the City of San Francisco. Described by: Anne Adams Date: Sept 26,Upper Steel Arch Bridge. Walter D’Arcy Ryan was the illuminating engineer and A.D. Dickerson who was his New York field assistant directed the scintillator. with information from American Technological Sublime by David E. Nye and the Niagara Falls info website

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A letter from 2nd Vice President and General Manager of Canadian General Electric Company, Frederic Nicholls to W. B. Rankine regarding a bid for contract. The letter mentions that the bid for two alternating generators for the Canadian side of Niagara Falls was won by Westinghouse Eletric and Manufacturing Co. Nicholls also mentions that there will be other opportunites to win contracts as more machines are required. Nicholls also implies that Westinghouse may have bid under cost in an effort to secure the first of many contracts with the Canadian Niagara Power Company.

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This work presents a neural network based on the ART architecture ( adaptive resonance theory), named fuzzy ART& ARTMAP neural network, applied to the electric load-forecasting problem. The neural networks based on the ARTarchitecture have two fundamental characteristics that are extremely important for the network performance ( stability and plasticity), which allow the implementation of continuous training. The fuzzy ART& ARTMAP neural network aims to reduce the imprecision of the forecasting results by a mechanism that separate the analog and binary data, processing them separately. Therefore, this represents a reduction on the processing time and improved quality of the results, when compared to the Back-Propagation neural network, and better to the classical forecasting techniques (ARIMA of Box and Jenkins methods). Finished the training, the fuzzy ART& ARTMAP neural network is capable to forecast electrical loads 24 h in advance. To validate the methodology, data from a Brazilian electric company is used. (C) 2004 Elsevier B.V. All rights reserved.

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The objective of this work is the development of a methodology for electric load forecasting based on a neural network. Here, it is used Backpropagation algorithm with an adaptive process based on fuzzy logic. This methodology results in fast training, when compared to the conventional formulation of Backpropagation algorithm. Results are presented using data from a Brazilian Electric Company and the performance is very good for the proposal objective.

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This work presents a procedure for electric load forecasting based on adaptive multilayer feedforward neural networks trained by the Backpropagation algorithm. The neural network architecture is formulated by two parameters, the scaling and translation of the postsynaptic functions at each node, and the use of the gradient-descendent method for the adjustment in an iterative way. Besides, the neural network also uses an adaptive process based on fuzzy logic to adjust the network training rate. This methodology provides an efficient modification of the neural network that results in faster convergence and more precise results, in comparison to the conventional formulation Backpropagation algorithm. The adapting of the training rate is effectuated using the information of the global error and global error variation. After finishing the training, the neural network is capable to forecast the electric load of 24 hours ahead. To illustrate the proposed methodology it is used data from a Brazilian Electric Company. © 2003 IEEE.

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Pages 137-144 blank for additional recipes.

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Cover title: Transistor manual, including tunnel diodes; specifications, applications, circuits.