Review of Optical Character Recognition for Power System Image Based on Artificial Intelligence Algorithm

Optical Character Recognition(OCR)refers to a technology that uses image processing technology and character recognition algorithms to identify characters on an image.This paper is a deep study on the recognition effect of OCR based on Artificial Intelligence(AI)algorithms,in which the different AI algorithms for OCR analysis are classified and reviewed.Firstly,the mechanisms and characteristics of artificial neural network-based OCR are summarized.Secondly,this paper explores machine learning-based OCR,and draws the conclusion that the algorithms available for this form of OCR are still in their infancy,with low generalization and fixed recognition errors,albeit with better recognition effect and higher recognition accuracy.Finally,this paper explores several of the latest algorithms such as deep learning and pattern recognition algorithms.This paper concludes that OCR requires algorithms with higher recognition accuracy.

Ultrafast power Doppler imaging for ischemic encephalopathy:A case report

BACKGROUND Severely elevated intracranial pressure due to various reasons,such as decreased cerebral perfusion,can lead to devastating neurological outcomes,such as brain herniation.Decompression craniectomy is a life-saving procedure that is commonly performed for such a critical situation,but the changes in cerebral microvessels after brain herniation and decompression are unclear.Ultrafast power Doppler imaging(uPDI)is a new microvascular imaging technology that utilizes high frame rate plane/diverging wave transmission and advanced clutter filters.uPDI significantly improves Doppler sensitivity and can detect microvessels,which are usually invisible using traditional ultrasound Doppler imaging.CASE SUMMARY In this report,uPDI was used for the first time to observe the brain blood flow of a hypoperfusion area in a 4-year-old girl who underwent decompression craniectomy due to refractory intracranial hypertension(ICP)after malignant brain tumor surgery.B-mode imaging was used to verify the increased densities of the cerebral cortex and basal ganglia that were observed by computed tomography.CONCLUSION uPDI showed the local blood supplies and anatomical structures of the patient after decompressive craniectomy.uPDI is potentially a more intuitive and noninvasive method for evaluating the effects of severe ICP on cerebral microvessels.

CMOS vision sensor with fully digital image process integrated into low power 1/8-inch chip

A digital still camera image processing system on a chip, different from the video camera system, is pre- sented for mobile phone to reduce the power consumption and size. A new color interpolation algorithm is proposed to enhance the image quality. The system can also process fixed patten noise （FPN） reduction, color correction, gamma correction, RGB/YUV space transfer, etc. The chip is controlled by sensor regis- ters by inter-integrated circuit （I2C） interface. The voltage for both the front-end analog and the pad cir- cuits is 2.8 V, and the volatge for the image signal processing is 1.8 V. The chip running under the external 13.5-MHz clock has a video data rate of 30 frames/s and the measured power dissipation is about 75 roW.

Improving contrast and sectioning power in confocal imaging by third harmonic generation in SiOx nanocrystallites

We present a new optical microscope in which the light transmitted by a sample-scanned transmission confocal microscope is frequency-tripled by SiOx nanocrystallites in lieu of being transmitted by a confocal pinhole. This imaging technique offers an increased contrast and a high scattered light rejection. It is demonstrated that the contrast close to the Sparrow resolution limit is enhanced and the sectioning power are increased with respect to the linear confocal detection mode. An experimental implementation is presented and compared with the conventional linear confocal mode.

A Data-driven Method for Transient Stability Margin Prediction Based on Security Region

Transient stability assessment(TSA)based on security region is of great significance to the security of power systems.In this paper,we propose a novel methodology for the assessment of online transient stability margin.Combined with a geographic information system(GIS)and transformation rules,the topology information and pre-fault power flow characteristics can be extracted by 2 D computer-vision-based power flow images(CVPFIs).Then,a convolutional neural network(CNN)-based comprehensive network is constructed to map the relationship between the steady-state power flow and the generator stability indices under the anticipated contingency set.The network consists of two components:the classification network classifies the input samples into the credibly stable/unstable and uncertain categories,and the prediction network is utilized to further predict the generator stability indices of the categorized samples,which improves the network ability to distinguish between the samples with similar characteristics.The proposed methodology can be used to quickly and quantitatively evaluate the transient stability margin of a power system,and the simulation results validate the effectiveness of the method.