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In many production processes a quantity of liquid is passed through a controlled channel. To keep the measured volume constant, a control device that controls the flow of liquid through the channel is used.
In some applications the need to control the flow is a given. The liquid can be a fluid, which can be a gas or a liquid. In some applications, the liquid may be a chemical product, such as a diluting or coloring solution, or a cleaning solution. In other applications, the liquid may be a filler material, such as a weighting material.
Some applications use gravity to pull liquid down a pipe. In such a case, a meter or gauge that measures the flow rate is usually located at the bottom of the pipe.
The channel of the apparatus may have a complex configuration. The channel may comprise a fixed, straight part and a part that moves. The moving part can be a weir, a flexible hose that controls the liquid flow rate. The moving part can be a piston, cylinder or diaphragm.
In some applications, the channel of the apparatus can be a tube that is shaped to change the flow of the liquid to the point at which it enters a measuring chamber.
In many applications, the liquid may be corrosive. For example, the liquid may be a cleaning solution or a diluting solution.
The measuring device usually comprises one or more valves that are actuated with an electric current, a pneumatic pressure or by a mechanical drive.
One prior art approach is to use an analog controller. A prior art analog controller usually comprises a current or voltage source, a mechanical drive (such as a motor), a reduction gear and a potentiometer.
The apparatus is calibrated on the production line and the calibration process is monitored by a separate 384a16bd22
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We present a new approach to pattern recognition in high dimensions, which achieves data compression with the dimensions of the patterns. Unlike the conventional methods such as the principal component analysis, the proposed method achieves data compression in a “multi-level” structure, resulting in a hierarchical pattern representation that preserves the rich set of low-level structure of patterns in the high-dimensional space.
Based on this idea, a new pattern representation method named Hierachical Dimensionality Reduction (HDR) is proposed. In the method, a pattern in a high-dimensional space is expanded into a set of patterns in a low-dimensional space by a sequence of thresholding operations. Then, the expanded patterns are successively compressed through a hierarchical pattern representation, which is achieved by a process of data compressing each expanded pattern by a thresholding operation. Finally, a pattern classifier can be trained to recognize the patterns.
In the proposed method, first, we have proposed a general objective function for pattern recognition, which is defined in a weighted Hilbert space. The general objective function has two parts: the first is to find a set of patterns that are closest to the given pattern; the second is to form a decision function. The general objective function can be defined in any Hilbert space, but we focus on the weighted Hilbert space.
For high-dimensional data, its data can be processed using only the high-level structure, i.e., low-level structure can be ignored. This idea is considered as a generalization of the principal component analysis and has been proposed. Our previous work has already demonstrated that the general objective function has advantages over the principal component analysis.
In the proposed method, after finding the high-level structure, i.e., low-level structure, of each pattern, we continue the process of data compressing, until we can identify the patterns. The final objective function consists of two parts: the first part is to find the high-level structure of a given pattern. The second part is to recognize the pattern by the high-level structure.
Dimensionality Reduction for Pattern Recognition Algorithm Description:
The proposed method for pattern recognition is based on the general objective function. We define the objective function as follows:
The objective function given in (1) is minimized under the condition that the distortion function given by equation (2) is minimized.
The objective function given by equation (1) is minimized under the conditions given by equations (3) and (4). The objective function can be easily minimized by
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