Machine learning

We use features such as asymmetric expression and crooked eyes to assess the risk of facial stroke. Observing the micro vibration of the head caused by the contraction of the heart, and develop a zero-contact facial heart rate and respiration rate detection technology in conjunction with the camera. The technology can accurately measure heart rate and respiration rate in real time, thereby reducing the risk of infection. This technology has obtained two ROC patents (M590433, I689285), two US patents (HEART RATE DETECTION METHOD AND DEVICE THEREOF,MOUTH AND NOSE OCCLUDED DETECTING METHOD AND SYSTEM THEREOF). The possibility of detecting strokes through AI machine learning methods is not only accurate, but also find out signs of stroke early to grasp the best time to seek medical treatment.

As for the proposed technology by combining the artificial bee colony algorithmthe semi-supervised extreme learning machine (ABC-SSELM), characteristic parameter normalization equations of I-V curves are tuned via low-cost data under normal operation of PV strings. The proposed ABC-SSELM method only needs 1-3 labeled data of the total dataset to save humantime cost. The accuracy of diagnosing various mixed faults can reach more than 99.84,the monitoring of dust accumulation can provide effective cleaning to increase the revenue of the solar PV power generation system.

This technique employs AIbig data analytics to precisely forecast cooling load demandestimates the efficiency of different chiller combinations. Time-of-Use Pricingoptimal chiller load-interval are also considered for practical needs. Decision supports of optimal chiller configuration is provided to enhance energy conservation. Moreover, using real data feedbackmodel health examination, the model can do self-calibrationmake evolution, continuously learn from the experts,provide decision supports in good quality.

Low-power deep learning accelerator integrates neural network design / training, model compressionaccelerator design. It uses a small amount of computationmemory footprint to realize high performance on the edge device. The image semantic segmentation technology can reach 80 frames per second (resolution:1024*2048) to meet real-time requirements for autonomous driving.

This technology aims to automatically extractdefine features from a data-driven perspective through a series of data engineering, machine learningensemble learning technologies via the big data collected from equipment sensorsquality characteristic measurement results. For products that have no quality measurement, a virtual metrology resultpredicted confidence score are provided as a decision-making basis for process control.

PCA-BM includes two models: “Automatic BMs Segmentation AI Model”" Distant Brain Failure Prediction Model". The former one uses C2FNAS (coarse to fine network architecture search) to detect the location, size,number of brain metastases. The latter uses radiomics to extract numerous radiographic featuresemploys machine learning methods such as XGBoost to establish a prognostic model of brain metastases. PCA-BM provides more precision treatment decisions for patientsimproves personalizedaccurate overall stereotactic radiosurgery planning.