Biomimetic Octopus Suction Cup with Attachment Force Self-Sensing Capability for Cardiac Adhesion.

This study develops a biomimetic soft octopus suction device with integrated self-sensing capabilities designed to enhance the precision and safety of cardiac surgeries. The device draws inspiration from the octopus's exceptional ability to adhere to various surfaces and its sophisticated proprioceptive system, allowing for real-time adjustment of adhesive force. The research integrates thin-film pressure sensors into the soft suction cup design, emulating the tactile capabilities of an octopus's sucker to convey information about the contact environment in real time. Signals from sensors within soft materials exhibiting complex strain characteristics are processed and interpreted using the grey wolf optimizer-back propagation (GWO-BP) algorithm. The tissue stabilizer is endowed with the self-sensing capabilities of biomimetic octopus suckers, and real-time feedback on the adhesion state is provided. The embedding location of the thin-film pressure sensors is determined through foundational experiments with flexible substrates, standard spherical tests, and biological tissue trials. The newly fabricated suction cups undergo compression pull-off tests to collect data. The GWO-BP algorithm model accurately identifies and predicts the suction cup's adhesion force in real time, with an error rate below 0.97% and a mean prediction time of 0.0027 s. Integrating this technology offers a novel approach to intelligent monitoring and attachment assurance during cardiac surgeries. Hence, the probability of potential cardiac tissue damage is reduced, with future applications for integrating intelligent biomimetic adhesive soft robotics.

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.

Optical fiber shape sensing of polyimide skin for a flexible morphing wing: retraction.

The referenced article [Appl. Opt.56, 9325 (2017)APOPAI0003-693510.1364/AO.56.009325] has been retracted by the authors.

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.

Diverse spectral band-based deep residual network for tongue squamous cell carcinoma classification using fiber optic Raman spectroscopy.

The research is to propose a new classification framework, called diverse spectral band-based deep residual network (DSB-ResNet), which can distinguish tongue squamous cell carcinoma (TSCC) from non-cancerous tissue. A fiber optic Raman spectroscopy system is used to collect Raman spectral data of TSCC and normal tissues. DSB-ResNet takes advantage of diverse spectral band-based spectra without processing to derive spectral representations from different spectral bands of Raman spectra, which improves the ability to identify TSCC. To show the superiority of the proposed method, the existing methods are used as the competitive methods to compare with the DSB-RestNet, the results demonstrate our method has the highest performance with 97.38 %, 98.75 %, and 98.25 % for sensitivity, specificity, and accuracy, respectively. The experimental results show that the DSB-ResNet is able to distinguish TSCC from non-cancerous tissue successfully. The proposed method is expected to provide a theoretical and methodological base for accurate detection of TSCC.

Deep convolutional neural networks for tongue squamous cell carcinoma classification using Raman spectroscopy.

With deep convolutional neural networks and fiber optic Raman spectroscopy, this study presents a novel classification method that discriminates tongue squamous cell carcinoma (TSCC) from non-tumorous tissue. To achieve this purpose, 24 tissues spectral data were first collected from 12 patients who had undergone a surgical resection due to the tongue squamous cell carcinomas. Then 6 blocks with each block including 1 convolutional layer and 1 max-pooling layer are used to extract the nonlinear feature representations from Raman spectra. The derived features form a representative vector, which is fed into a fully-connected network for performing classification task. Experimental results demonstrated that the proposed method achieved high sensitivity (99.31%) and specificity (94.44%). To show the superiority for the ConvNets classifier, comparison results with the state-of-the-art methods show it had a competitive classification accuracy. Moreover, these promising results may pave the way to apply the deep ConvNets model in the fiber optic Raman instrument for intra-operative evaluation of TSCC resection margins and improve patient survival.

Optical Fiber Sensor Performance Evaluation in Soft Polyimide Film with Different Thickness Ratios.

To meet the application requirements of curvature measurement for soft biomedical robotics and flexible morphing wings of aircraft, the optical fiber Bragg grating (FBG) shape sensor for soft robots and flexible morphing wing was implemented. This optical FBG is embedded in polyimide film and then fixed in the body of a soft robot and morphing wing. However, a lack of analysis on the embedded depth of FBG sensors in polyimide film and its sensitivity greatly limits their application potential. Herein, the relationship between the embedded depth of the FBG sensor in polyimide film and its sensitivity and stability are investigated. The sensing principle and structural design of the FBG sensor embedded in polyimide film are introduced; the bending curvatures of the FBG sensor and its wavelength shift in polyimide film are studied; and the relationship between the sensitivity, stability, and embedded depth of these sensors are verified experimentally. The results showed that wavelength shift and curvature have a linear relationship. With the sensor's curvature ranging from 0 m-1 to 30 m-1, their maximum sensitivity is 50.65 pm/m-1, and their minimum sensitivity is 1.96 pm/m-1. The designed FBG sensor embedded in polyimide films shows good consistency in repeated experiments for soft actuator and morphing wing measurement; the FBG sensing method therefore has potential for real applications in shape monitoring in the fields of soft robotics and the flexible morphing wings of aircraft.

Optical fiber shape sensing of polyimide skin for a flexible morphing wing.

This paper presents the 3D shape sensing of polyimide thin film skin for a flexible morphing wing using fiber Bragg grating (FBG) sensors. The calibration curves of the FBG sensors are measured experimentally to ensure relative accurate conversion between Bragg wavelength shift (BWS) and bending curvature of the polyimide skin. The reflection spectra of the FBG sensors are measured at different airfoil profiles, and the variation tendency of the BWS values with the airfoil profiles are analyzed. The bending curvatures of the polyimide thin film skin at different airfoil profiles are calculated using the measured BWS values of the FBG sensors and the linear interpolation algorithm. The 3D shapes of the polyimide skin at different airfoil profiles are reconstructed based on the measured bending curvatures and the interpolation and curve fitting functions. The 3D precise visual measurements are conducted using a digital photogrammetry system, and then the correctness of the shape reconstruction results are verified. The results prove that the maximum error between the 3D visual and FBG measurements is less than 5%. The FBG sensing method is effective for the shape sensing of polyimide skin for flexible morphing wing.

Step-heating infrared thermographic inspection of steel structures by applying least-squares regression.

This paper reports the application of the least-squares regression method in the step-heating thermographic inspection of steel structures. The surface temperature variation of a slab with finite thickness during both the step-heating phase and the cooling-down phase is presented. A mild steel slab with holes of various depths and diameters is chosen as the specimen. The step-heating thermographic inspection experiments are carried out on the specimen with different heating times. The heating as well as the cooling-down phases are recorded with an infrared camera and are analyzed separately by linear regression of the double logarithmic temperature increase versus time plots. Three statistics of the linear regression, the slope, the coefficient of determination, and the F-test value, are used to create image maps according to the processing results. The signal-to-noise ratio of each map is calculated to evaluate the performance of the three imaging methods with different durations of heating time and cooling time. The results prove that the F-test value maps present a good performance for the sequences of the step-heating phase, while the slope maps present a good performance for the sequences of the cooling-down phase. The optimal heating time and cooling time for a steel structure are also concluded. The comparison with the results of the thermographic signal reconstruction (TSR) method proves that the least-squares regression method has better detectability and a higher inspection efficiency.

Inspection of disbonds in multilayer dissimilar metal structure using lock-in thermography.

This paper reports the characterization of disbonds between a steel plate and a lead plate on the surface of the lead using lock-in thermography. Based on the photothermal model, the excitation frequency of the bonding specimen with the steel plate and the lead plate is optimized. A lock-in thermography testing system is established to inspect the specimen. The Fourier transform method is used to process the infrared image sequences. To improve the signal-to-noise ratio (SNR), a fuzzy c-means (FCM) algorithm is used to process the phase matrix. The influence of number of clusters on the processing results is researched, and the optimal value of clusters number is obtained. The shearing phase technique is used to evaluate the size of the disbonds quantitatively. The measurement results of the artificial disbonds in the specimen show good agreement with the actual values. The results prove that FCM is effective in enhancing the SNR of the phase image, which makes it feasible for the quantitative determination of defect size by the shearing phase technique.

Detection of drilling-induced delamination in aeronautical composites by noncontact laser ultrasonic method.

A novel application of the laser ultrasonic technique for the detection of drilling-induced delamination in composite components of aircrafts is proposed. Numerous key components of aircrafts are made of composite materials, and drilling is often a final operation during assembly. Drilling-induced delamination significantly reduces the structural reliability, and it is rather difficult to be detected effectively and automatically. The laser ultrasonic technique is a promising method to solve the problem. This paper investigates the characterization of drilling-induced delamination in composites by a noncontact laser ultrasonic method. A carbon fiber reinforced plastic laminate with drilling holes is prepared as a specimen. The characterization of drilling-induced delamination with laser-generated ultrasonic waves is investigated theoretically and experimentally, and the morphology features of the delamination are obtained by laser ultrasonic C-scan testing. The results prove that the laser ultrasonic technique is effective for the detection of drilling-induced delamination in composite components, and it is a feasible solution for evaluating the drilling quality during assembly.

Ultrasonic characterization of delamination in aeronautical composites using noncontact laser generation and detection.

The characterization of delamination in composite plates with ultrasonic waves generated and detected by lasers is presented. Composite materials have become one of the most important structural materials in the aviation industry because of their excellent mechanical properties, such as high specific stiffness and antifatigue. This paper reports a new application of the laser ultrasonic technique to perform nondestructive detection of carbon-fiber-reinforced plastic (CFRP) and continuous-fiber-reinforced ceramic matrix composites (CFCCs) containing artificial internal defects, based on propagation characteristic of ultrasonic waves generated by pulse laser with a wavelength of 1064 nm and pulse duration of 10 ns. A laser interferometer based on two-wave mixing is used to measure ultrasonic wave signals. The main advantage of this technique over conventional ultrasonic testing techniques is the ability to carry out detection without using coupling agents. The research results prove that the laser ultrasonic technique is effective for the detection of internal defects in both CFRP and CFCC composite components, which should promote and expand the application of the technique in the aviation industry.