A high-efficiency primary-side regulation control strategy for half-bridge LLC resonant converter with wide input voltage range.

The requirement of the half-bridge LLC resonant converter with a wide input voltage range is becoming higher in photovoltaic applications because of its simple structure and low switching loss. Conventional frequency modulation (FM) requires a wide switching frequency range and a high-quality factor circuit design, leading to reduced efficiency and large component volumes at light loads. To solve the problems, a high-efficiency control strategy using adaptive pulse-width and frequency modulation (APWFM) is proposed. APWFM adjusts the gain by changing the switching frequency and duty cycle simultaneously. When the output power is below the reference value, the switching frequency decreases linearly as the output power decreases, and the duty cycle is simultaneously modulated to achieve constant output voltage, so the switching frequency variation range is smaller than FM. This results in improved light or medium load efficiency in a limited frequency range while keeping a small volume of magnetic components. Also, the proposed control strategy is realized with primary-side regulation (PSR) to eliminate the optocoupler and simplify the control circuit. Experimental results demonstrate a significant improvement in efficiency at medium and light loads compared to FM, and the average efficiency is improved by 5% based on low cost and simple operation.

Deep neural network training method based on vectorgraphs for designing of metamaterial broadband polarization converters.

In this work, we proposed a method of extracting feature parameters for deep neural network prediction based on the vectorgraph storage format, which can be applied to the design of electromagnetic metamaterials with sandwich structures. Compared to current methods of manually extracting feature parameters, this method can automatically and precisely extract the feature parameters of arbitrary two-dimensional surface patterns of the sandwich structure. The position and size of surface patterns can be freely defined, and the surface patterns can be easily scaled, rotated, translated, or transformed in other ways. Compared to the pixel graph feature extraction method, this method can adapt to very complex surface pattern design in a more efficient way. And the response band can be easily shifted by scaling the designed surface pattern. To illustrate and verify the method, a 7-layer deep neural network was built to design a metamaterial broadband polarization converter. Prototype samples were fabricated and tested to verify the accuracy of the prediction results. In general, the method is potentially applicable to the design of different kinds of sandwich-structure metamaterials, with different functions and in different frequency bands.

Transformer- and Generative Adversarial Network-Based Inpatient Traditional Chinese Medicine Prescription Recommendation: Development Study.

BACKGROUND: Traditional Chinese medicine (TCM) practitioners usually follow a 4-step evaluation process during patient diagnosis: observation, auscultation, olfaction, inquiry, pulse feeling, and palpation. The information gathered in this process, along with laboratory test results and other measurements such as vital signs, is recorded in the patient's electronic health record (EHR). In fact, all the information needed to make a treatment plan is contained in the EHR; however, only a seasoned TCM physician could use this information well to make a good treatment plan as the reasoning process is very complicated, and it takes years of practice for a medical graduate to master the reasoning skill. In this digital medicine era, with a deluge of medical data, ever-increasing computing power, and more advanced artificial neural network models, it is not only desirable but also readily possible for a computerized system to mimic the decision-making process of a TCM physician. OBJECTIVE: This study aims to develop an assistive tool that can predict prescriptions for inpatients in a hospital based on patients' clinical EHRs. METHODS: Clinical health records containing medical histories, as well as current symptoms and diagnosis information, were used to train a transformer-based neural network model using the corresponding physician's prescriptions as the target. This was accomplished by extracting relevant information, such as the patient's current illness, medicines taken, nursing care given, vital signs, examinations, and laboratory results from the patient's EHRs. The obtained information was then sorted chronologically to produce a sequence of data for the patient. These time sequence data were then used as input to a modified transformer network, which was chosen as a prescription prediction model. The output of the model was the prescription for the patient. The ultimate goal is for this tool to generate a prescription that matches what an expert TCM physician would prescribe. To alleviate the issue of overfitting, a generative adversarial network was used to augment the training sample data set by generating noise-added samples from the original training samples. RESULTS: In total, 21,295 copies of inpatient electronic medical records from Guang'anmen Hospital were used in this study. These records were generated between January 2017 and December 2018, covering 6352 types of medicines. These medicines were sorted into 819 types of first-category medicines based on their class relationships. As shown by the test results, the performance of a fully trained transformer model can have an average precision rate of 80.58% and an average recall rate of 68.49%. CONCLUSIONS: As shown by the preliminary test results, the transformer-based TCM prescription recommendation model outperformed the existing conventional methods. The extra training samples generated by the generative adversarial network help to overcome the overfitting issue, leading to further improved recall and precision rates.