Automatic recognition methods for non-stationary electroencephalogram (EEG) data collected from EEG sensors play an essential role in neurological detection. The integrated approaches proposed in this study consist of Symlet wavelet processing, a gradient boosting machine, and a grid search optimizer for a three-class classification scheme for normal subjects, intermittent epilepsy, and continuous epilepsy. Fourth-order Symlet wavelets are adopted to decompose the EEG data into five frequencies sub-bands, such as gamma, beta, alpha, theta, and delta, whose statistical features were computed and used as classification features. The grid search optimizer is used to automatically find the optimal parameters for training the classifier. The classification accuracy of the gradient boosting machine was compared with that of a conventional support vector machine and a random forest classifier constructed according to previous descriptions. Multiple performance indices were used to evaluate the proposed classification scheme, which provided better classification accuracy and detection effectiveness than has been recently reported in other studies on three-class classification of EEG data.

Intelligent recognition methods for classifying non-stationary and non-invasive epileptic diagnoses are essential tools in neurological research. Electroencephalogram (EEG) signals exhibit better temporal characteristics in the detection of epilepsy compared to radiation medical images like computed tomography (CT) and magnetic resonance imaging (MRI), as they provide real-time insights into the disease’ condition. While classical machine learning methods have been used for epilepsy EEG classification, they still often require manual parameter adjustments. Previous studies primarily focused on binary epilepsy recognition (epilepsy vs. healthy subjects) rather than as ternary status recognition (continuous epilepsy vs. intermittent epilepsy vs. healthy subjects). In this study, we propose a novel deep learning method that combines a convolution neural network (CNN) with a long short-term memory (LSTM) network for multi-class classification including both binary and ternary tasks, using a publicly available benchmark database on epilepsy EEGs. The hybrid CNN-LSTM automatically acquires knowledge without the need for extra pre-processing or manual intervention. Besides, the joint network method benefits from memory function and stronger feature extraction ability. Our proposed hybrid CNN-LSTM achieves state-of-the-art performance in ternary classification, outperforming classical machine learning and the latest deep learning models. For the three-class classification, in the method achieves an accuracy, specificity, sensitivity, and ROC of 98%, 97.4, 98.3% and 96.8%, respectively. In binary classification, the method achieves better results, with ACC of 100%, 100%, and 99.8%, respectively. Our dual stream spatiotemporal hybrid network demonstrates superior performance compared to other methods. Notably, it eliminates the need for manual operations, making it more efficient for doctors to diagnose during the clinical process and alleviating the workload of neurologists.

Soft linear actuators have strong deformation ability and good environmental adaptability, which have been widely used in soft robot design. However, little work has focused on designing soft linear actuators with balanced performances, featuring fast driving speed, large output displacement, being lightweight, and miniaturization. Herein, we present a novel soft linear actuator design based on the Kresling origami structure. By driving the Kresling tubes with a servo motor, the soft linear actuator has good compliance and strong environmental adaptability and can achieve a driving speed, large driving force, and high control precision comparable to the traditional electrical motor. The analytical models of the Kresling tubes and the whole actuator are respectively derived to analyze the mechanical properties, determine the optimal geometry of the Kresling tube, and evaluate the driving performance of the whole actuator. The actuator prototype is fabricated by 3D printing, and the actual driving performance is tested. It is shown that the prototype can achieve a maximum output displacement of 18.9 mm without payload or 16 mm under a payload of 30 N. Finally, as a case study, the soft linear actuator is applied to a crawling robot, where the maximum moving speed of 28 mm/s is reached.

In multi-vehicle systems, the coupling problem between the task allocation and path planning and the variability of task execution solutions creates challenges for utility estimation and affects the effectiveness of distributed mission planning. To characterize the effect of task sequences on the task utilities and implement a task-extended distributed allocation, we propose a task-extended utility tensor algorithm (TEUTA) based on market mechanism. In the mission planning problem of multi-vehicle system, we consider the impact of the task schedule on the vehicle trajectory, and indicate the vehicle task execution utilities under different preceding task points in the form of tensors. Further, a task-extended utility tensor iterative algorithm (TEUTIA) is presented based on an iterative strategy to improve the algorithm in terms of computational complexity. A task execution utility estimation model and an algorithm framework are designed for the implementation of the two proposed algorithms. The simulation and experimental results show that compared with the non-tensor method, TEUTA and TEUTIA can achieve higher task execution performance, and TEUTIA has better computational efficiency. Note to Practitioners —This work presents two novel multi-vehicle distributed mission planning algorithms based on the market mechanism. TEUTA and TEUTIA proposed in this paper can be applied to address the impact of vehicle motion constraints on mission planning, which are widely present in various types of common nonholonomic vehicles such as two-wheel differential drive vehicles and Ackerman steering vehicles. In the application of the algorithms, an accurate kinematic model of the vehicles is required for trajectory planning to estimate the task execution reward precisely, which is necessary for effective mission planning. When the vehicle trajectory planning algorithm is computationally intensive, TEUTIA can significantly reduce the computational consumption and improve the mission planning efficiency compared with TEUTA without losing task execution reward. Finally, a stable inter-vehicle communication network is required for the interactive process of the market mechanism, where bi-directional communication exists between any two vehicles, to ensure the stability of mission planning.

Automatic recognition methods for non-stationary EEG data collected from EEG sensors play an essential role in neurological detection. The integrative approaches proposed in this study consists of Symlet wavelet processing, a gradient boosting machine, and a grid search optimizer for a three-level classification scheme for normal subjects, intermittent epilepsy, and continuous epilepsy. Fourth-order Symlet wavelets were adopted to decompose the EEG data into five time-frequency sub-bands, whose statistical features were computed and used as classification features. The grid search optimizer was used to automatically find the optimal parameters for training the classifier. The classification accuracy of the gradient boosting machine was compared with that of a support vector machine and a random forest classifier constructed according to previous descriptions. Multiple-index were used to evaluate the Symlet wavelet transform-gradient boosting machine-grid search optimizer classification scheme, which provided better classification accuracy and detection effectiveness than has recently reported in other work on three-level classification of EEG data.