Zhiyong Zhou

In view of the stress of people's life in modern life, they frequently suffer from mental illness, such as depression, autism, mental illness and a series of problems. A design of an embedded real-time controllable multi-channel transcranial DC stimulator system based on STM32 is proposed. The DC stimulator is controlled by the STM32 microcontroller, which integrates the power supply module, communication module, impedance monitoring module, etc. Intelligent algorithms are used to optimize human-machine interaction in software design. The user can adjust the treatment parameters through the relevant control interface and intelligent treatment through a combination of treatment channels and treatment modes.

Objectives/Hypothesis: To develop a deep‐learning‐based automatic diagnosis system for identifying nasopharyngeal carcinoma (NPC) from noncancer (inflammation and hyperplasia), using both white light imaging (WLI) and narrow‐band imaging (NBI) nasopharyngoscopy images.Study Design: Retrospective study.Methods: A total of 4,783 nasopharyngoscopy images (2,898 WLI and 1,885 NBI) of 671 patients were collected and a novel deep convolutional neural network (DCNN) framework was developed named Siamese deep convolutional neural network (S‐DCNN), which can simultaneously utilize WLI and NBI images to improve the classification performance. To verify the effectiveness of combining the above‐mentioned two modal images for prediction, we compared the proposed S‐DCNN with two baseline models, namely DCNN‐1 (only considering WLI images) and DCNN‐2 (only considering NBI images).Results: In the threefold cross‐validation, an overall accuracy and area under the curve of the three DCNNs achieved 94.9% (95% confidence interval [CI] 93.3%–96.5%) and 0.986 (95% CI 0.982–0.992), 87.0% (95% CI 84.2%–89.7%) and 0.930 (95% CI 0.906–0.961), and 92.8% (95% CI 90.4%–95.3%) and 0.971 (95% CI 0.953–0.992), respectively. The accuracy of S‐DCNN is significantly improved compared with DCNN‐1 (P‐value <.001) and DCNN‐2 (P‐value = .008).Conclusion: Using the deep‐learning technology to automatically diagnose NPC under nasopharyngoscopy can provide valuable reference for NPC screening. Superior performance can be obtained by simultaneously utilizing the multimodal features of NBI image and WLI image of the same patient.Level of Evidence: 3 Laryngoscope, 132:999–1007, 2022

In view of the lack of objective data support for product evaluation methods in the industry, a triangular verification method was proposed; it considered nursing beds as the study object and combined subjective evaluation with eye movement and electroencephalogram. Because the triangular validation method is based on the numerical value between the indicators and the frequency of ranking, this method is worth investigating for analyzing experimental data more scientifically. This paper focuses on the further analysis of the experimental data, especially the use of interval estimation method. After analysis, we obtain that proposal 2 is the optimal solution. This method is more suitable for product evaluation which will collect large amount of experimental data to obtain more accurate results. For industrial product designers, the evaluation of products by users is very important. In the design stage, how to grasp the user’s evaluation of the product more accurately is a difficult problem. This paper takes nursing bed as the research object and studies the user participation design in order to make the product more acceptable to most people after it is launched.

Finding exact solutions of nonlinear equations plays an important role in nonlinear science, especially in engineering and mathematical physics. In this paper, we employed the complex method to get eight exact solutions of the modified BBM equation for the first time, including two elliptic function solutions, two simply periodic solutions, and four rational function solutions. We used the exp-expansion methods to get fourteen forms of solutions of the modified BBM equation. We also used the sine-cosine method to obtain eight styles’ exact solutions of the modified BBM equation. Only the complex method can obtain elliptic function solutions. We believe that the complex method presented in this paper can be more effectively applied to seek solutions of other nonlinear evolution equations.

The dynamic detection of human motion is important, which is widely applied in the fields of motion state capture and rehabilitation engineering. In this study, based on multimodal information of surface electromyography (sEMG) signals of upper limb and triaxial acceleration and plantar pressure signals of lower limb, the effective virtual driving control and gait recognition methods were proposed. The effective way of wearable human posture detection was also constructed. Firstly, the moving average window and threshold comparison were used to segment the sEMG signals of the upper limb. The standard deviation and singular values of wavelet coefficients were extracted as the features. After the training and classification by optimized support vector machine (SVM) algorithm, the real-time detection and analysis of three virtual driving actions were performed. The average identification accuracy was 90.90%. Secondly, the mean, standard deviation, variance, and wavelet energy spectrum of triaxial acceleration were extracted, and these parameters were combined with plantar pressure as the gait features. The optimized SVM was selected for the gait identification, and the average accuracy was 90.48%. The experimental results showed that, through different combinations of wearable sensors on the upper and lower limbs, the motion posture information could be dynamically detected, which could be used in the design of virtual rehabilitation system and walking auxiliary system.