Advantech (Taiwan)

Timely intervention in shock is vital, as delays over one hour greatly increase mortality. This study aims to develop an enhanced machine learning model that improves predictive performance by utilizing self-controlled data and applying feature engineering informed by medical knowledge to physiological waveforms, enabling the prediction of shock one hour in advance without relying on blood tests. Patient data and physiological waveforms were obtained from the Medical Information Mart for Intensive Care III (MIMIC-3) database. Shock was defined as a mean arterial pressure ≤ 65 mmHg for more than one minute, combined with serum lactate levels ≥ 2 mmol/L within 12h before or after the hypotension event. Waveforms used for prediction were extracted from 30min time-segment before a 1-hour period prior to the event. Self-controlled waveforms were obtained from the same patient either one day before or up to seven days after the shock event. The study included 389 ICU patients who met the shock criteria and had complete physiological waveform data available for analysis. A total of 299 features were derived: 90 from arterial blood pressure (ABP), 89 from electrocardiogram (ECG), 112 from respiratory waveforms (RESP), and 8 from blood oxygen saturation (SpO2). The weighted ensemble model showed the best performance with an AUC of 0.93 and accuracy of 84.15%, and sensitivity of 79.64% in the testing set. The most predictive features included ECG_HRV_pNN50 (proportion of successive heartbeat intervals differing by more than 50 ms), RESP_Width_Mean (mean width of respiratory waveform), RESP_Cycle_Rate_Mean (mean respiratory cycle rate), ABP_TimeSBP2DBP_SampEn (sample entropy of systolic-diastolic intervals), and ABP_AmplitudeDBP_Median (median amplitude of diastolic peaks). This study demonstrated the feasibility of predicting shock one hour before its onset using only four physiological waveforms, combined with feature engineering based on physiological concepts and self-sampling data. The model achieved a strong AUC and a high sensitivity. Not applicable.

A deep ensemble convolutional neural network (CNN) model to inspect printed circuit board (PCB) board dual in-line package (DIP) soldering defects with Hybrid-YOLOv2 (YOLOv2 as a foreground detector and ResNet-101 as a classifier) and Faster RCNN with ResNet-101 and Feature Pyramid Network (FPN) (FRRF) achieved a detection rate of 97.45% and a false alarm rate (FAR) of 20%–30% in the previous study [34] . However, applying the method to other production lines, environmental variations, such as lighting, orientations of the sample feeds, and mechanical deviations, led to the degradation in detection performance. This article proposes an effective self-adaption method that collects “exception data” like the samples with which the Artificial Intelligent (AI) model made mistakes from the automated optical inspection inference edge to the training server, retraining with exceptions on the server and deploying back to the edge. The proposed defect detection system has been verified with real tests that achieved a detection rate of 99.99% with an FAR 20%–30% and less than 15 s of inspection time on a resolution $7296 \times 6000$ PCB image. The proposed system has proven capable of shortening inspection and repair time for online operators, where a 33% efficiency boost from the three production lines of the collaborated factory has been reported [6] . The contribution of the proposed retraining mechanism is threefold: 1) because the retraining process directly learns from the exceptions, the model can quickly adapt to the characteristic of each production line, leading to a fast and reliable mass deployment; 2) the proposed retraining mechanism is a necessary self-service for conventional users as it incrementally improves the detection performance without professional guidance or fine-tuning; and 3) the semiautomatic exception data collection method helps to reduce the time-consuming manual labeling during the retraining process.

The conventional PCB (Printed Circuit Board) DIP (Dual Inline Package) process solder defect detection was done by labor inspection, which is not only time-intensive but also labor-intensive. This paper proposes a deep ensemble method to inspect the PCB solder defects to replace the labor inspection. To achieve a high detection rate and a low false alarm rate, two distinct detection models, a hybrid YOLOv2 (YOLOv2 as a foreground detector and ResNet-101 as a classifier) and Faster RCNN with ResNet-101 and FPN are separately trained to obtain a high detection rate result. The final ensemble model aggregates the result from the two detection models. That achieves a 96.73% detection rate and a 19.73% false alarm rate in real tests. The detection time is less than 15 seconds for inferencing a PCB image with a resolution of 7296*6000. The proposed method has been proven efficient in terms of guiding operators to identify and fix PCB solder defects [1] and thus is able to reduce 33% of labor demand for each PCB production line at our real test site. [1].