A Novel Catheter Shape-Sensing Method Based on Deep Learning with a Multi-Core Optical Fiber.

In this paper, we propose a novel shape-sensing method based on deep learning with a multi-core optical fiber for the accurate shape-sensing of catheters and guidewires. Firstly, we designed a catheter with embedded multi-core fiber containing three sensing outer cores and one temperature compensation middle core. Then, we analyzed the relationship between the central wavelength shift, the curvature of the multi-core Fiber Bragg Grating (FBG), and temperature compensation methods to establish a Particle Swarm Optimization (PSO) BP neural network-based catheter shape sensing method. Finally, experiments were conducted in both constant and variable temperature environments to validate the method. The average and maximum distance errors of the PSO-BP neural network were 0.57 and 1.33 mm, respectively, under constant temperature conditions, and 0.36 and 0.96 mm, respectively, under variable temperature conditions. This well-sensed catheter shape demonstrates the effectiveness of the shape-sensing method proposed in this paper and its potential applications in real surgical catheters and guidewire.

Temperature-compensated fiber-optic online monitoring methodology for 3D shape and strain of near-space airship envelope.

Near-space airships are high-end airships that are being vigorously developed in the aerospace industry. It has important application value in the telecommunication, surveillance, monitoring, remote sensing, and exploration fields. The envelope is the key component that provides lift to the airship. Online monitoring of envelope status is critical to ensuring airship performance, safety, and reliability. However, online monitoring of the 3D shape and strain of the airship envelope is still a challenging task. A hybrid multi-core and single-core fiber-optic monitoring method with a temperature self-compensation function is proposed to address this issue. The method uses multi-core fiber optic sensors, 3D curves, and a surface reconstruction algorithm to obtain the 3D shape of the envelope. Temperature decoupling of the sensing signal is carried out via sensors on the central core of the multi-core fibers that are only sensitive to temperature, thereby eliminating the influence of temperature changes on the measurement accuracy. The strain field of the envelope skin is measured by single-core fiber optic sensors and a strain interpolation algorithm. The accuracy of the proposed method is experimentally validated. The results show that the 3D shape measurement error of the envelope skin is 4.82% when the skin is bent in the range of 10m -1-15.38m -1. When the ambient temperature changes in the range of -50∘ C-150∘ C, the position measurement error caused by the temperature change is only 1.2% of the effective measurement length (160 mm) of the multi-core fiber optic sensor. When the skin is stretched in the range of 500-5000µÎµ, the measurement error of the average value of the skin strain field is only 0.75%. This proves that the proposed method can simultaneously measure the 3D shape and strain field of the envelope skin and also effectively suppress the influence of ambient temperature changes on the measurement accuracy. The proposed method has application prospects in the online monitoring of airship envelopes.

Shape reconstruction based on a multicore optical fiber array with temperature self-compensation.

Temperature variations affect the accuracy of fiber-optic shape sensors; thus, temperature compensation is particularly important. This study developed a temperature self-compensation algorithm and verified the measuring accuracy of shape sensors after temperature compensation. A multicore fiber Bragg grating (FBG) sensor array was calibrated to confirm the consistency of sensor characteristics, and the relationship between the curvature and wavelength shift of FBGs was studied. A variable-temperature experiment revealed the temperature sensitivity of the FBG sensors, and these results were used by the temperature self-compensation algorithm. Further, shape reconstruction before and after temperature compensation was studied. The deformed shapes of the multicore FBG sensor array under different bending conditions were reconstructed. The results obtained after temperature compensation show that the average error between the measured and the theoretical coordinate values as less than 0.33 mm, the maximum error as less than 5.61 mm, and the relative error as less than 3.50%. The proposed temperature self-compensation algorithm has excellent prospects for application to flexible structures.

A parallel particle swarm optimization and enhanced sparrow search algorithm for unmanned aerial vehicle path planning.

Unmanned Aerial Vehicle (UAV) path planning is to plan an optimal path for its flight in a specific environment. But it cannot get satisfactory results using ordinary algorithms. To solve this problem, a hybrid algorithm is proposed named as PESSA, where particle swarm optimization (PSO) and an enhanced sparrow search algorithm (ESSA) work in parallel. In the ESSA, the random jump of the producer's position is strengthened to guarantee the global search ability. Each scrounger keeps learning from the vintage experience of the producers. For the best-positioned sparrow, when it perceives the threat, the difference between the best individual and the worst individual will be imposed to speed up the search process. The elite reverse search strategy was added to yields the optimum diversity. In this paper, the performance of the PESSA algorithm is verified by 10 basic functions, and it can find the optimal value on the 7 test functions. Compared with the other 12 algorithms, PESSA's average value always ranks first. Finally, the proposed PESSA is applied in 4 different scenarios including two groups of 2D environments and two groups of 3D environments. In 2D environments, the average optimization results can reach 0.0165 and 0.0521 in two cases respectively. In 3D environments, the average optimization results can reach 0.6635 and 0.5349 in two cases respectively. The results show that the PESSA algorithm can acquire more feasible and effective route than compared algorithms.

Octopus-inspired sucker to absorb soft tissues: stiffness gradient and acetabular protuberance improve the adsorption effect.

Rigid suckers commonly used in surgical procedures often cause absorption damage, while their soft counterparts are difficult to handle due to their weak anchoring. Alternatively, the octopus sucker is both soft and has strong suction power. Further observation revealed that its structure is self-sealing and that the tissues are layered in hardness. Inspired by said structure and the characteristics of associated materials, a bionic soft sucker with stiffness gradient and acetabular roof structure was proposed, made of silicone with varying hardness including structures such as acetabular roof and circle muscles. The automatic tensile force measurement system was used to experimentally analyze the adsorption performance of the suckers to the soft curved contact surface. Both dry and wet conditions were tested, along with practical tests on organisms. The bionic sucker adsorption force was increased by 25.1% and 34.6% on the cylindrical surface, and 45.2% and 7.3% on the spherical surface for dry and wet conditions, respectively. During the experiment, the bionic suckers did not cause notable suction damage to the contact surfaces. Thus, this type of bionic sucker shows good application prospects in the field of surgery.