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The efficiency of vibration control in an automotive semi-active suspension system depends on the quality of information from sensors installed in the vehicle, including information about deflection of the suspension system. The control algorithm for vibration attenuation of the body takes into account its velocity as well as the relative velocity of the suspension. In this paper it is proposed to use the Linear Variable Differential Transformer (LVDT) unit to measure the suspension deflection and then to estimate its relative velocity. This approach is compared with a typical solution implemented in such applications, where the relative velocity is calculated by processing signals acquired from accelerometers placed on the body and on the chassis. The experiments performed for an experimental All-Terrain Vehicle (ATV) confirm that using LVDT units allows for improving ride comfort by better vibration attenuation of the body.

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In this paper, authors described methods of material granularity evaluation and a novel method for grain size determination with inline electromagnetic mill device diagnostics. The milling process quality evaluation can be carried out with vibration measurements, analysis of the milling material images or well-known screening machines. The method proposed in this paper is developed to the online examination of the milled product during the milling process using real-time digital images. In this paper, authors concentrated their work on copper ore milling process. Determination of the total number of the grain, the size of each grain, also the classification of the grains are the main goal of the developed method. In the proposed method the vision camera with lightning mounted at two assumed angles has been used. The detection of the grains has been based on an adaptive segmentation algorithm, improved with distance transform to enhance grains detection. The information about particles shape and context is used to optimize the grain classification process in the next step. The final classification is based on the rule-based method with defined particle shape and size parameters. [ABSTRACT FROM AUTHOR]

This paper presents a machine vision method for detection and classification of copper ore grains. We proposed a new method that combines both seeded regions growing segmentation and edge detection, where region growing is limited only to grain boundaries. First, a 2D Fast Fourier Transform (2DFFT) and Gray-Level Co-occurrence Matrix (GLCM) are calculated to improve the detection results and processing time by eliminating poor quality samples. Next, detection of copper ore grains is performed, based on region growing, improved by the first and second derivatives with a modified Niblack's theory and a threshold selection method. Finally, all the detected grains are characterized by a set of shape features, which are used to classify the grains into separate fractions. The efficiency of the algorithm was evaluated with real copper ore samples of known granularity. The proposed method generates information on different granularity fractions at a time with a number of grain shape features. [ABSTRACT FROM AUTHOR]