There are many sources of disturbances for on-the-go weighing. This study was conducted to develop a new method to remove those disturbances and to estimate more accurate dynamic masses in silage harvesting. A mathematically simple procedure was developed using vertical movement information from a low-cost accelerometer, in addition to conventional instrumentation using load cells for mass data, and was tested using a small scale model weighing bin and a commercial silage wagon. Clear similarities were found between the patterns of low-pass filtered mass and acceleration data when both were obtained simultaneously from the same harvesting system. Multiplication factors for acceleration data were calculated so that the differences between the mass data and the multiplied acceleration data could be minimized. Thus, subtracting the multiplied acceleration from the mass data corresponded to deleting load cell disturbances due to vertical movements. A small-scale model weighing system was used to apply the developed approach to known dynamic masses. Mass estimation showed less than 20 g measurement errors for experimental loads. The same method was applied to a commercial silage wagon with harvested silage mass data in the 0 to 7,000 kg range. The proposed method showed that remaining error magnitudes were reduced by 39% to 56% and standard deviations were reduced by 53% to 68 % with respect to the results of low-pass filtering and moving average.

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We present a pipeline monitoring system based on bio-memetic robot in this paper. A bio-memetic robot exploring pipelines measures temperature, humidity, and vibration. The principal function of pipeline monitoring robot for the exploring pipelines is to recognize the shape of pipelines. We use infrared distance sensor to recognize the shape of pipelines and potentiometer to measure the angle of motor mounting infrared distance sensor. For the shape recognition of pipelines, the number of detected pipelines is used during only one scanning of distance. Three fuzzy classifiers are used for the number of detected pipelines, and the classifying results are presented in this paper.

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