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The clinical sub-speciality departments are increasing. It is usually difficult for patients without medical education to choose a suitable department when they need to make an online registration with doctor. A novel approach is presented for departments recommendation in this paper. The ICD codes and symptoms of medical health data are used to describe the patients’ characteristics, diseases, and departments in the quantitative relation. The knowledge graph is built with the relations to automatically recommend departments. The MacBERT-BiLSTM-CRF medical entity recognition model (MacNER) is proposed to identify the patient’s symptoms, parts, treatment methods and drug entities to structure a knowledge graph. The knowledge graph is used to provide knowledge for the intelligent department recommendation. The identified entities are mapped to nodes in the graph with the entity mapping method. Finally, an algorithm named Feature Rate Multiply Converse Disease Rate (FRMCDR) is proposed. The most appropriate department can be recommended by fusing the patient’s chief complaint and past medical history. The experiment shows that our method obtained an 88.77% precision rate in the recommendation.

This paper investigates Kalman Filter-based Heuristic Ensemble (KFHE), which is a new perspective on multi-class ensemble classification with performance significantly better or at least as good as the state-of-the-art algorithms. We prove that the sample weight tuning method used in KFHE is a version of adaptive boosting, and the weight distribution does not change anymore and leads to redundant classifiers when the algorithm iterates enough times. This motivates us to select a sub-ensemble to alleviate the redundancy and improve the performance of the ensemble. An Ordering-based Kalman Filter Selective Ensemble (OKFSE) is proposed in this paper to select a sub-ensemble using the margin distance minimization approach. We demonstrate the effectiveness and robustness of OKFSE through extensive experiments on 20 real-world UCI datasets, and the statistical test shows that OKFSE significantly outperforms the state-of-the-art KFHE and clustering-based pruning methods on these datasets with 5% and 10% class label noise.

In this paper, we proposed an enhanced secure scheme for the wireless communication system threatened by an intelligent attacker, which can work in eavesdropping, jamming, and spoofing modes. The conventional secure scheme is to apply Q-learning-based algorithm to reach a Nash equilibrium (NE) in the framework of a zero-sum game between the transmitter and attacker, which, however, requires the number of antennas at the transmitter to be much larger than that at the attacker. To overcome this limitation, we first consider the scenario where the attacker can flexibly increase the number of antennas in order to increase the attack rate. By adaptively setting the number of antennas at the transmitter and the legitimate receiver equal to that at the attacker, we then apply the beamforming at the transmitter to suppress the eavesdropping and use the filtering at the receiver to prevent the jamming and spoofing. By incorporating the beamforming and filtering, the benefits of the attacker in this game are efficiently restricted. Furthermore, the Q-learning-based power control strategy is used to reach a new NE. The simulation results have been demonstrated to show that the proposed scheme can suppress the intelligent attack efficiently, which outperforms the conventional scheme in the secrecy performance.

In this paper, a biomimetic flexible fishtail actuated by shape memory alloy (SMA) wires is presented. The detailed structure of the fishtail propeller is designed to mimic the muscle and bone structure of a Carassius auratus. The mechanical modeling of the biomimetic fishtail is built; then, constitutive equations and thermal models of embedded SMA wires are described. Next, experiments are carried out to analyze the propulsive performance of the fishtail. Its maximum bending angle is 50° under the conditions of a heating voltage of 14.8 V and a heating time of 175 ms at a water temperature of 24.5°C. The symmetrical characteristic of both sides of the biomimetic fishtail is good, and different fluctuation can be adjusted by changing the heating time. The bending time and recovery time are recorded, and the effect of heating time on oscillation frequency is analyzed. Finally, the propulsive forces of three different heating modes at frequencies of 0.4 Hz, 0.5 Hz and 0.6 Hz are tested and compared. A relatively good heating mode is discovered for controlling the biomimetic fishtail during the experiments. The biomimetic fishtail obtains the largest average propulsive force of 0.041 N at a frequency of 0.8 Hz with a heating time of 80 ms and a bending angle of 22°.

Recently, the non-orthogonal multiple access (NOMA) scheme has been applied in millimeter-wave (mmWave) communication system to support more users and further improve the performance. In this paper, an energy-efficient power allocation (PA) scheme is designed for a downlink multi-user mmWave-NOMA system with hybrid precoding (HP), where two typical HP architectures, namely the fully-connected HP architecture (FHPA) and the sub-connected HP architecture (SHPA), are both considered. Firstly, we pair users in term of their channel difference and correlation. Then, analog beamforming schemes are proposed for the system with fully-connected and sub-connected HP architectures, respectively. Based on this, a two-step HP design with the proposed analog beamforming and zero-forcing precoding is presented. With these results, the optimization problem is formulated to maximize the energy efficiency (EE) of the system. This is a non-convex optimization problem, and can be approximately decomposed into independent convex sub-problems by applying the fractional programming theory. Using the coordinate descent method, the closed-form solutions of each sub-problem are derived. On these basis, an effective iterative algorithm is proposed to obtain the suboptimal power allocation. Simulation results verify the effectiveness of the proposed PA scheme, it has the same EE performance as the existing scheme with relatively low complexity, and can obtain the EE close to the exhaustive search scheme as well as the chaotic accelerated particle swarm optimization scheme. Moreover, the proposed analog beamforming obviously outperforms the conventional finite resolution analog beamforming, and the system with SHPA has higher EE than that with FHPA.

In air combat, target intent recognition is the premise and foundation of battlefield situation awareness and intelligent decision-making. Aiming at the problem that traditional intention recognition methods cannot deal with a large amount of continuous target data, an air target combat intention recognition model based on one-dimensional convolutional neural networks and bidirectional long short-term memory (1DCNN-BiLSTM) is proposed. First, the target data is divided into fixed-size continuous subsequences by time sliding window on the basis of determining the target feature space and intention space. Second, the convolution operation is performed on the target sequence through the 1DCNN module as a means of extracting the features of the target attributes in the time dimension, and at the same time reducing the dimensionality of the target sequence, so as to facilitate the subsequent processing of the target data. Then, the BiLSTM module is utilized to capture the dependencies on the longer distance of the target sequence from both forward and reverse directions simultaneously. Finally, the optimal model structure and hyperparameters of 1DCNN-BiLSTM are not only determined through experiments, but also the validity of each part of the model is verified. Compared with the traditional methods, the model proposed in this paper effectively improves the accuracy of combat intention recognition of air targets, and provides the essential basis and auxiliary support for the decision-making of the commanders.

Considering the uncertainties caused by multiple concurrent complex environments, we investigate the robust adaptive control of multi-source uncertain systems under unmeasurable states and input saturation. For the unmeasurable states caused by sensor faults, a state observer is proposed to provide state estimation values for the controller; for the compound uncertain functions composed of nonlinear uncertain parameters and environmental disturbances, an optimal fuzzy approximation strategy is designed to reproduce these uncertain functions. Finally, the state observer with optimal approximation errors is proved to be stable. Subsequently, the low-pass filter, dynamic surface, and backstepping joint control scheme ensures the robust stability of the uncertain systems, while avoiding the differential explosion caused by disturbances. A series of hierarchical adaptive laws enable the controller to adjust parameters to achieve an optimal tracking of the entire process of time-varying reference output. The compensation algorithm designed with a Nussbaum function solves the problem of input saturation caused by actuator faults. Finally, the nonlinear uncertain systems based on fuzzy adaptive backstepping achieve robust tracking control of time-varying signals. The stability is proved by Lyapunov functions, and the effectiveness of the method simulation is verified using hypersonic vehicle model as an example.