초록 ▼

To prevent temporary reduction of battery voltage when driving a shutter, an exposure control drive apparatus is provided with a boosting circuit for boosting a voltage of the battery and outputting a boosted voltage. A capacitor is charged by the boosted voltage for driving an actuator of an exposu

To prevent temporary reduction of battery voltage when driving a shutter, an exposure control drive apparatus is provided with a boosting circuit for boosting a voltage of the battery and outputting a boosted voltage. A capacitor is charged by the boosted voltage for driving an actuator of an exposure control mechanism. A detecting circuit detects a current flowing in the boosting circuit and a control circuit controls operation of the boosting circuit in accordance with a magnitude of the detected value so that the boosting circuit is prevented from boosting the battery voltage when the detected current exceeds a predetermined level and is permitted to boost the battery voltage when the detected current is below the predetermined level. In this manner, the current flowing from the battery is prevented from exceeding a maximum level.

대표청구항 ▼

To prevent temporary reduction of battery voltage when driving a shutter, an exposure control drive apparatus is provided with a boosting circuit for boosting a voltage of the battery and outputting a boosted voltage. A capacitor is charged by the boosted voltage for driving an actuator of an exposu

To prevent temporary reduction of battery voltage when driving a shutter, an exposure control drive apparatus is provided with a boosting circuit for boosting a voltage of the battery and outputting a boosted voltage. A capacitor is charged by the boosted voltage for driving an actuator of an exposure control mechanism. A detecting circuit detects a current flowing in the boosting circuit and a control circuit controls operation of the boosting circuit in accordance with a magnitude of the detected value so that the boosting circuit is prevented from boosting the battery voltage when the detected current exceeds a predetermined level and is permitted to boost the battery voltage when the detected current is below the predetermined level. In this manner, the current flowing from the battery is prevented from exceeding a maximum level. said classification output by referencing said multi-dimensional wavelet neural network to provide a final declaration as to whether the classification output should be classified as a defect. 2. An apparatus according to claim 1 wherein said at least one sensor comprises a CCD array camera that captures said 2-D representation. 3. An apparatus according to claim 1 further comprising a light source that produces signals having a frequency in either the infared or visible range and wherein said at least one sensor comprises a vision camera responsive to said signals in the infared or visible frequency range. 4. An apparatus according to claim 3 further comprising a light diffuser disposed between said light source and said object for controlling the uniformity and intensity of the signals being projected onto the object. 5. An apparatus according to claim 1 comprising an analog to digital converter that converts said 2-D representation to a digital representation and provides said digital representation to said memory, and wherein said processor derives said at least one signal from said digital representation. 6. An apparatus according to claim 1 wherein said processor derives said at least one signal by projecting said 2-D representation into a 1-D signal. 7. An apparatus according to claim 6 wherein said processor derives said 1-D signal from said 2-D representation by utilizing a projection from the group consisting of vertical scanning, horizontal scanning and fractal scanning. 8. An apparatus according to claim 6 wherein said processor generates some of said features by deriving values from said 1-D signal. 9. An apparatus according to claim 6 wherein said processor generates a feature vector by storing said feature values in an array. 10. An apparatus according to claim 1 wherein said processor determines whether at least one of said features is within a range indicative of a predetermined fault pattern and does not provide said features to said multi-dimensional wavelet neural network if said at least one feature is not within said range. 11. An apparatus according to claim 1 further comprising a decision logic module that receives said classification output from said multi-dimensional wavelet neural network and said features to determine the degree of certainty that said pattern recognition system determines that said predetermined pattern was detected. 12. An apparatus according to claim 1 wherein the object is textile fabric being manufactured by warp knitting. 13. An apparatus according to claim 1 wherein the object is greige fabric. 14. An apparatus according to claim 1 wherein the object is textile fabric being inspected on a device selected from a group consisting of a circular loom, loom, an off-line inspection station or a weaving machine. 15. The apparatus of claim 1, the program module further comprising instructions to provide for a learning element wherein extraction is based on features present in the multi-dimensional wavelet neural network which features are updated based on the resolution of conflicts in the classification output concerning prior derived signals from said logic unit. 16. A method for recognizing defects in an object, comprising: generating a 2-D digital representation of at least part of an object, the digital representation comprising a plurality of pixels; extracting a plurality of features from said 2-D digital representation based on classification characteristics; providing said features to a multi-dimensional wavelet neural network, said multidimensional wavelet neural network incorporating a learning technique from the following group consisting of structure learning, parameter learning, or combined parameter and structure learning for classification performance; comparing said features to a predetermined fault pattern to determine if the feature represents a defect, and providing said features and said classification output from said multi-dimensional wavelet neural ne