In bio-inspired design, identifying keywords is an important step of biological information searching. Based on existing information retrieval approaches, the amount of relevant biological information in a search result largely depends on the identified keywords. Due to the limitation of biological knowledge, design engineers are difficult to identify the appropriate keywords that can find the biological information related to engineering requirements. To address this issue, we present an algorithm that can calculate the Composite Correlation Intension of functionally combined words, which integrates semantic similarity computation, data normalization, and corpus technology. Based on the algorithm, a method that automatically pushes keywords for biological information searching in bio-inspired design is also proposed. The method decomposes engineering requirements and structures functionally combined words, calculates the Composite Correlation Intension values of all functionally combined words, ranks the functionally combined words by the Composite Correlation Intension values, takes the functionally combined words with larger Composite Correlation Intension values as keywords, and pushes them. Through these keywords, design engineers retrieve the relevant biological information and discover the required design knowledge. In order to facilitate the use of proposed method, an auxiliary tool was developed in Python environment. Finally, the possibility of proposed method was demonstrated by a preliminary validation and an application case. The results show that the proposed method would be a promising alternative to identify keywords for biological information searching in bio-inspired design.

The quality of seed pieces is crucial for potato planting. Each seed piece should contain viable potato eyes and maintain a uniform size for mechanized planting. However, existing intelligent methods are limited by a single view, making it difficult to satisfy both requirements simultaneously. To address this problem, we present an intelligent 3D potato cutting simulation system. A sparse 3D point cloud of the potato is reconstructed from multi-perspective images, which are acquired with a single-camera rotating platform. Subsequently, the 2D positions of potato eyes in each image are detected using deep learning, from which their 3D positions are mapped via back-projection and a clustering algorithm. Finally, the cutting paths are optimized by a Bayesian optimizer, which incorporates both the potato’s volume and the locations of its eyes, and generates cutting schemes suitable for different potato size categories. Experimental results showed that the system achieved a mean absolute percentage error of 2.16% (95% CI: 1.60–2.73%) for potato volume estimation, a potato eye detection precision of 98%, and a recall of 94%. The optimized cutting plans showed a volume coefficient of variation below 0.10 and avoided damage to the detected potato eyes, producing seed pieces that each contained potato eyes. This work demonstrates that the system can effectively utilize the detected potato eye information to obtain seed pieces containing potato eyes and having uniform size. The proposed system provides a feasible pathway for high-precision automated seed potato cutting.

Eddy current testing technology is widely used in the defect detection of conductive material and the integrity assessment of key components. Because the eddy current signal is easily affected by the complex electromagnetic environment during the testing process, it causes a lot of noise in the signals, which affects the analysis of eddy current signals. Based on the sparse and redundant representation of eddy current signals, a dictionary learning method based on K-means singular value decomposition (K-SVD) for adaptive sparse representation is studied. The noisy signals are sparsely decomposed on the redundant dictionary constructed by learning, and then the sparse representation vector is reconstructed, and the noise is processed into the residual to be discarded in the reconstruction process, thereby achieving the separation of the signal and the noise to achieve the denoising effect of the eddy current signal. Finally, the experimental results verify the accuracy and effectiveness of the proposed method in eddy current signal denoising.

Arterial stenosis caused by calcified plaque that grows inside the vasculature would decrease the blood flow, many medical methods are used to remove the plaque to improve the blood flow. Atherectomy is an efficient way to remove the plaque with lower risk of relapse. However, a great deal of fragments are generated by the conventional atherectomy product during cutting the calcified plaque off, and these fragments should be collected by other surgery equipment. A novel coring atherectomy system (NCAS) is proposed to improve this situation, the NCAS consists of a coring rotablation, soft drive shaft with center hole and negative pressure unit. The maximum work torque of the soft drive shaft is calculated, and the effect of the rotablation rotation speed on removing the plaque is evaluated. The artery phantom is simulated by different size of silicon tube, and the plaque phantoms with different stenosis grade are fabricated by gelatin, CaCO3 and wax. Finally, the experiment setup is built up to evaluate the performance of NCAS. The experiment results show that the NCAS could remove the plaque efficiently, and more than 80% the fragment is stored in the catheter which would be expelled outside the body directly. Otherwise, we found that the soft plaque could be sucked into the catheter without atherectomy by a negative pressure unit.

Pursuit–evasion tasks involving multiple unmanned surface vehicles (USVs) in complex maritime environments present significant challenges in terms of adaptability, intelligence, and robustness. This paper proposes a novel multi-layer formation control method to significantly improve the maneuverability of USV formation in dynamic pursuit scenarios. The primary contributions of this work are threefold: (1) The development of a maneuverable pursuit–evasion decision layer that enables strategic encirclement of the evader is proposed, which can flexibly adjust the encirclement strategy for the evader and reduce the resource consumption of USVs based on the Apollonius circle-based multiple-angle encirclement strategy and the artificial potential field (APF). (2) A coordination layer based on the trajectory prediction of the evader is designed. This layer introduces a Head-Guidance (HG) operator into the APF to dynamically predict the evader trajectory with motion constraints and assist the Pursuit team to complete tasks. (3) A novel pursuit–evasion distributed Model Predictive Control (MPC) layer that ensures robust formation tracking and collision avoidance is proposed. This layer is specifically engineered to handle not only dynamic obstacle constraints but also lumped uncertainties. The overall performance of the proposed multi-layer framework is verified via abundant numerical simulations under complex marine disturbances and multiple obstacle environments, and the developed fixed-time extended state observer is validated with a real-water field experiment with a single self-developed USV prototype.