High-precision all-in-one dual robotic arm strategy in oral implant surgery.

INTRODUCTION: Dental implantation has emerged as an efficient substitute for missing teeth, which is essential for restoring oral function and aesthetics. Compared to traditional denture repair approaches, dental implants offer better stability and sustainability. The position, angle, and depth of dental implants are crucial factors for their long-term success and necessitate high-precision operation and technical support. METHOD: We propose an integrated dual-arm high-precision oral implant surgery navigation positioning system and a corresponding control strategy. Compared with traditional implant robots, the integrated dual-arm design greatly shortens the preparation time before surgery and simplifies the operation process. We propose a novel control flow and module for the proposed structure, including an Occluded Target Tracking Module (OTTM) for occlusion tracking, a Planting Plan Development Module (PPDM) for generating implant plans, and a Path Formulation Module (PFM) for controlling the movement path of the two robot arms. RESULT: Under the coordinated control of the aforementioned modules, the robot achieved excellent accuracy in clinical trials. The average angular error and entry point error for five patients who underwent implant surgery using the proposed robot were 2.1° and 0.39 mm, respectively. CONCLUSION: In essence, our study introduces an integrated dual-arm high-precision navigation system for oral implant surgery, resolving issues like lengthy preoperative preparation and static surgical planning. Clinical results confirm its efficacy, emphasizing its accuracy and precision in guiding oral implant procedures.

ResNet for recognition of Qi-deficiency constitution and balanced constitution based on voice.

Background: According to traditional Chinese medicine theory, a Qi-deficiency constitution is characterized by a lower voice frequency, shortness of breath, reluctance to speak, an introverted personality, emotional instability, and timidity. People with Qi-deficiency constitution are prone to repeated colds and have a higher probability of chronic diseases and depression. However, a person with a Balanced constitution is relatively healthy in all physical and psychological aspects. At present, the determination of whether one has a Qi-deficiency constitution or a Balanced constitution are mostly based on a scale, which is easily affected by subjective factors. As an objective method of diagnosis, the human voice is worthy of research. Therefore, the purpose of this study is to improve the objectivity of determining Qi-deficiency constitution and Balanced constitution through one's voice and to explore the feasibility of deep learning in TCM constitution recognition. Methods: The voices of 48 subjects were collected, and the constitution classification results were obtained from the classification and determination of TCM constitutions. Then, the constitution was classified according to the ResNet residual neural network model. Results: A total of 720 voice data points were collected from 48 subjects. The classification accuracy rate of the Qi-deficiency constitution and Balanced constitution was 81.5% according to ResNet. The loss values of the model training and test sets gradually decreased to 0, while the ACC values of the training and test sets tended to increase, and the ACC values of the training set approached 1. The ROC curve shows an AUC value of 0.85. Conclusion: The Qi-deficiency constitution and Balanced constitution determination method based on the ResNet residual neural network model proposed in this study can improve the efficiency of constitution recognition and provide decision support for clinical practice.

Control system research in wave compensation based on particle swarm optimization.

For the offshore wave compensation control system, its controller setting will directly affect the platform's compensation effect. In order to study the wave compensation control system and optimization strategy, we build and simulate the wave compensation control model by using particle swarm optimization (PSO) to optimize the controller's control parameters and compare the results with other intelligent algorithms. Then we compare the response errors of the wave compensation platform under different PID controllers; and compare the particle swarm algorithm's response results and the genetic algorithm to the system controller optimization. The results show that the particle swarm algorithm is 63.94% lower than the genetic algorithm overshoot, and the peak time is 0.26 s lower, the adjustment time is 1.4 s lower than the genetic algorithm. It shows that the control effect of the wave compensation control system has a great relationship with the controller's parameter selection. Meanwhile, the particle swarm optimization algorithm's optimization can set the wave compensation PID control system, and it has the optimization effect of small overshoot and fast response time. This paper proposes the application of the particle swarm algorithm to the wave compensation system. It verifies the superiority of the method after application, and provides a new research reference for the subsequent research on the wave compensation control systems.

Low-Complexity High-Order Propagator Method for Near-Field Source Localization.

In this paper, an efficient high-order propagator method is proposed to localize near-field sources. We construct a specific non-Hermitian matrix based on the high-order cumulant of the received signals. With its columns and rows, we can obtain two subspaces orthogonal to all the columns of two steering matrices, respectively, with which the estimation of the directions of arrival (DOA) and ranges of near-field sources can be achieved. Different from other methods, the proposed method needs only one matrix for estimating two parameters separately, therefore leading to a smaller computational burden. Simulation results show that the proposed method achieves the same performance as the other high order statistics-based methods with a lower complexity.