1,337 research outputs found

    Fuzzy-Pattern-Classifier Based Sensor Fusion for Machine Conditioning

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    Effektive Nutzung des Leguminosenstickstoffs in der Fruchtfolge

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    Aus einer langjährigen Untersuchung lassen sich praxisrelevante Schlussfolgerungen zur effektiven Nutzung des Leguminosenstickstoffs ableiten. Fruchtfolgestellung und Bewirtschaftungsmaßnahmen haben einen erheblichen Einfluss. - Beste Nutzung des Stickstoffs durch Zwischenfrüchte - Winterwitterung beeinflusst besonders N-Gehalte im Frühjahr - Was ist für eine effektive N-Verwertung zu beachten

    Rückstandsmessungen an Welsfilets

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    Einfluss der ökologischen Bewirtschaftung auf die Nitratstickstoffgehalte im Boden

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    The following paper discribe the influence of ecological managed systems on contents of nitrate-nitrogen in soils. From 1993 to 2003 in a six-year crop rotation in the location of Gülzow (Mecklenburg-Vorpommern) samples of soil were taken with an soil auger in a depth between 0 and 90 cm. Nitrate-nitrogen contents was analyzed by hydrazin reduction in an analysing automat (SKALAR). For statistical evaluation SAS (Statistical Analysis System) and for clearing of unorthogonal data procedure MIXED of PIEPHO and MICHEL (2001) were used. The results show that contents of nitrate-nitrogen in autumn and spring are lower for organic management than for conventional. Nitrogen losses during winter are subjected to heavy annual variations. High contents of nitrate-nitrogen in the end of vegetation resulted in high losses on sandy soils in winter. The amount of contents of nitrate-nitrogen in the end of vegetation is heavily influenced by the crop rotation and the management connected with this crop. Danger of high losses of nitrate-nitrogen during the winter is especially big in the growing of winter grain with legumes grown before. Losses through leaching can be decreased by catch crops and by high degree of soil covering for example with clover and grass

    Cryogenic testing of a 25 kV RIS bushing

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    Fuzzy-Pattern-Classifier Training with Small Data Sets

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    It is likely in real-world applications that only little data isavailable for training a knowledge-based system. We present a method forautomatically training the knowledge-representing membership functionsof a Fuzzy-Pattern-Classification system that works also when only littledata is available and the universal set is described insufficiently. Actually,this paper presents how the Modified-Fuzzy-Pattern-Classifier’s member-ship functions are trained using probability distribution functions

    Thermal coal drying with the high volatile matter output

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    Наведено аналіз сучасних технологій сушіння вугілля з високим вмістом летких. Промислове застосування набули парові сушарки з киплячим шаром продуктивністю 55 т/год, в яких теплоносієм є інертний азот, а необхідне тепло отримують за рахунок відхідних газів газифікатора, при цьому теплопередача здійснюється за допомогою гарячої води.Приведен анализ современных технологий сушки углей с высоким содержанием летучих. Промышленное применение получили паровые сушилки с кипящим слоем производительностью 55 т/ч, в которых теплоносителем служит инертный азот, а необходимое тепло получают за счет отходящих газов газификатора, при этом теплопередача осуществляется посредством горячей воды.An analysis of modern technologies of drying of coals with high content of volatile. Industrial applications received steam dryer with a fluidized bed with capacity of 55 tons/h, in which the coolant is inert nitrogen, and the necessary heat is generated by waste gases of the gasifier, the heat transfer is carried out by means of hot water
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