Finite-time active disturbance rejection control scheme for quadrotor attitude tracking.

In this paper, a finite-time active disturbance rejection control (FADRC) framework for quadrotor attitude tracking is presented. The FADRC framework is developed for two cascaded loops (inner-loop and outer-loop) to control the attitude angle and angular velocity, respectively. Two nonlinear extended state observers (NESOs) are devised to actively estimate and compensate for total disturbances, including internal uncertainties and external disturbances.

A parallel tonotopically arranged thalamocortical circuit for sound processing.

The perception of the sensory world in mammals requires information flow from the thalamus to the cortex. Although the first-order sensory thalamus and its surrounding nuclei are considered the major hub for feedforward thalamocortical transmission, it remains unknown whether any other thalamic input could also contribute to this transmission. We found a thalamic region, the basal region of the ventromedial nucleus of the thalamus (bVM), that sends dense, tonotopically arranged projections to auditory cortex (AuC) fields.

An Improved Detecting Algorithm of Moving Targets for Airborne Maritime Surveillance Radar.

The traditional method is capable of detecting and tracking stationary and slow-moving targets in a sea surface environment. However, the signal focusing capability of such a method could be greatly reduced especially for those variable-speed targets. To solve this problem, a novel tracking algorithm combining range envelope alignment and azimuth phase filtering is proposed.

Limited Memory-Based Random-Weighted Kalman Filter.

The Kalman filter is an important technique for system state estimation. It requires the exact knowledge of system noise statistics to achieve optimal state estimation. However, in practice, this knowledge is often unknown or inaccurate due to uncertainties and disturbances involved in the dynamic environment, leading to degraded or even divergent filtering solutions.

Decoupling and Parameter Extraction Methods for Conical Micro-Motion Object Based on FMCW Lidar.

Micro-Doppler time-frequency analysis has been regarded as an important parameter extraction method for conical micro-motion objects. However, the micro-Doppler effect caused by micro-motion can modulate the frequency of lidar echo, leading to coupling between structure and micro-motion parameters. Therefore, it is difficult to extract parameters for micro-motion cones.

Storage reliability prediction of electromechanical components based on virtual manufacturing and testing.

Electromechanical components (EMCs) such as relays and contactors have been used extensively in industrial and military areas. The storage reliability of these EMCs has a direct impact on the reliability of the system that contains them. However, during the design phase, it is difficult to predict the storage reliability of EMCs because of few failure rate data of parts, as well as limited testing time and budgets.

A Clustering Scheme Based on the Binary Whale Optimization Algorithm in FANET.

With the continuous development of Unmanned Aerial Vehicle (UAV) technology, UAVs are widely used in military and civilian fields. Multi-UAV networks are often referred to as flying ad hoc networks (FANET). Dividing multiple UAVs into clusters for management can reduce energy consumption, maximize network lifetime, and enhance network scalability to a certain extent, so UAV clustering is an important direction for UAV network applications.

BrainS: Customized multi-core embedded multiple scale neuromorphic system.

Research on modeling and mechanisms of the brain remains the most urgent and challenging task. The customized embedded neuromorphic system is one of the most effective approaches for multi-scale simulations ranging from ion channel to network. This paper proposes BrainS, a scalable multi-core embedded neuromorphic system capable of accommodating massive and large-scale simulations.

Learning-based interfered fluid avoidance guidance for hypersonic reentry vehicles with multiple constraints.

To address the problem of no-fly zone avoidance for hypersonic reentry vehicles in the multiple constraints gliding phase, a learning-based avoidance guidance framework is proposed. First, the reference heading angle determination problem is solved efficiently and skillfully by introducing a nature-inspired methodology based on the concept of the interfered fluid dynamic system (IFDS), in which the distance and relative position relationships of all no-fly zones can be comprehensively considered, and additional rules are no longer needed. Then, by incorporating the predictor-corrector method, the heading angle corridor, and bank angle reversal logic, a fundamental interfered fluid avoidance guidance algorithm is proposed to steer the vehicle toward the target zone while avoiding no-fly zones.

A multiwavelet-based sparse time-varying autoregressive modeling for motor imagery EEG classification.

Brain-computer Interface (BCI) system based on motor imagery (MI) heavily relies on electroencephalography (EEG) recognition with high accuracy. However, modeling and classification of MI EEG signals remains a challenging task due to the non-linear and non-stationary characteristics of the signals. In this paper, a new time-varying modeling framework combining multiwavelet basis functions and regularized orthogonal forward regression (ROFR) algorithm is proposed for the characterization and classification of MI EEG signals.

A novel optimization method for belief rule base expert system with activation rate.

Although the belief rule base (BRB) expert system has many advantages, such as the effective use of semi-quantitative information, objective description of uncertainty, and efficient nonlinear modeling capability, it is always limited by the problem of combinatorial explosion. The main reason is that the optimization of a BRB with many rules will consume many computing resources, which makes it unable to meet the real-time requirements in some complex systems. Another reason is that the optimization process will destroy the interpretability of those parameters that belong to the inadequately activated rules given by experts.

[Automatic removal algorithm of electrooculographic artifacts in non-invasive brain-computer interface based on independent component analysis].

The non-invasive brain-computer interface (BCI) has gradually become a hot spot of current research, and it has been applied in many fields such as mental disorder detection and physiological monitoring. However, the electroencephalography (EEG) signals required by the non-invasive BCI can be easily contaminated by electrooculographic (EOG) artifacts, which seriously affects the analysis of EEG signals. Therefore, this paper proposed an improved independent component analysis method combined with a frequency filter, which automatically recognizes artifact components based on the correlation coefficient and kurtosis dual threshold.

Signal Source Localization with Long-Term Observations in Distributed Angle-Only Sensors.

Angle-only sensors cannot provide range information of targets and in order to determine accurate position of a signal source, one can connect distributed passive sensors with communication links and implement a fusion algorithm to estimate target position. To measure moving targets with sensors on moving platforms, most of existing algorithms resort to the filtering method. In this paper, we present two fusion algorithms to estimate both the position and velocity of moving target with distributed angle-only sensors in motion.

YOLO-SASE: An Improved YOLO Algorithm for the Small Targets Detection in Complex Backgrounds.

To improve the detection ability of infrared small targets in complex backgrounds, an improved detection algorithm YOLO-SASE is proposed in this paper. The algorithm is based on the YOLO detection framework and SRGAN network, taking super-resolution reconstructed images as input, combined with the SASE module, SPP module, and multi-level receptive field structure while adjusting the number of detection output layers through exploring feature weight to improve feature utilization efficiency. Compared with the original model, the accuracy and recall rate of the algorithm proposed in this paper were improved by 2% and 3%, respectively, in the experiment, and the stability of the results was significantly improved in the training process.

Attributes learning network for generalized zero-shot learning.

In the absence of unseen training data, zero-shot learning algorithms utilize the semantic knowledge shared by the seen and unseen classes to establish the connection between the visual space and the semantic space, so as to realize the recognition of the unseen classes. However, in real applications, the original semantic representation cannot well characterize both the class-specificity structure and discriminative information in dimension space, which leads to unseen classes being easily misclassified into seen classes. To tackle this problem, we propose a Salient Attributes Learning Network (SALN) to generate discriminative and expressive semantic representation under the supervision of the visual features.

[Structural design and performance analysis of an auxiliary dining robot].

An auxiliary dining robot is designed in this paper, which implements the humanoid feeding function with theory of inventive problem solving (TRIZ) theory and aims at the demand of special auxiliary nursing equipment. Firstly, this robot simulated the motion function of human arm by using the tandem joints of the manipulator. The end-effector used a motor-driven spoon to simulate the feeding actions of human hand.

Performance analysis of typical linear augmented observers for a class of MIMO systems with nonlinear uncertainty.

To estimate the states and the uncertainties for multi-input-multi-output (MIMO) systems is significant in control engineering. The paper investigates the estimating performance of a widely-used linear augmented observer (LAO) by quantitatively analyzing the biased term of estimation error, which generalizes the previous studies. Then the necessary and sufficient condition for unbiased estimation of LAO is thoroughly discussed.

Multi-view graph embedding clustering network: Joint self-supervision and block diagonal representation.

Multi-view clustering has become an active topic in artificial intelligence. Yet, similar investigation for graph-structured data clustering has been absent so far. To fill this gap, we present a Multi-View Graph embedding Clustering network (MVGC).

CNN-LRP: Understanding Convolutional Neural Networks Performance for Target Recognition in SAR Images.

Target recognition is one of the most challenging tasks in synthetic aperture radar (SAR) image processing since it is highly affected by a series of pre-processing techniques which usually require sophisticated manipulation for different data and consume huge calculation resources. To alleviate this limitation, numerous deep-learning based target recognition methods are proposed, particularly combined with convolutional neural network (CNN) due to its strong capability of data abstraction and end-to-end structure. In this case, although complex pre-processing can be avoided, the inner mechanism of CNN is still unclear.

On obstacle avoidance path planning in unknown 3D environments: A fluid-based framework.

Compared with preprocessed obstacle environments, unknown environments are more challenging for path planning. In unknown environments, an agent can make decisions only by relying on the obstacle information detected by its onboard sensors. However, when facing non-convex obstacles, this limited detection information can easily trap the agent in a local optimum.

An extended state observer for a class of nonlinear systems with a new frequency-domain analysis on convergence.

Regarding a class of multi-input and multi-output (MIMO) nonlinear systems, this paper presents a multi-variable linear extended state observer (MVLESO), which is applied to estimate uncertain dynamics of system real-timely. After that, a new frequency-domain analysis approach is explored to present the convergence property of MVLESO with respect to the estimation on uncertain dynamics. Meanwhile, the estimation error is mathematically modeled in frequency domain.

Investigations on system integration method and dynamic performance of electromechanical actuator.

A highly integrated electromechanical actuator was developed in this article, which aims at fulfilling the requirements of high power-to-weight ratio, high efficiency, high integration and low volume in military equipment. Three different transmission schemes were proposed for the integrated electromechanical actuator according to the differences in integration methods. Comparative analysis was conducted on the specific structures of the integrated electromechanical actuator and the categories and performance of the planetary roller screw, which is the key unit of the integrated electromechanical actuator.

An ascent guidance algorithm for the energy management of solid rockets.

Efficient energy management for the solid rocket is very important to exhaust excess energy and to meet the various terminal constraints. Based on the online planning of the velocity capability curve, a Double-arcs Energy Management (DAEM) guidance algorithm is proposed in this paper. In the DAEM, the velocity capability curve is designed with two tangential arcs.

Micro-Doppler Feature Extraction of Inverse Synthetic Aperture Imaging Laser Radar Using Singular-Spectrum Analysis.

Different from microwave radar, laser radar could be more sensitive to the micro-Doppler (m-D) effect due to its wave length. This limits the application of conventional methods, such as time⁻frequency based approach, since the processing needs a receiver with much higher sampling frequency than microwave radar. In this paper, a micro-Doppler feature extraction algorithm is proposed for the inverse synthetic aperture imaging laser radar (ISAIL).

Future Trend Forecast by Empirical Wavelet Transform and Autoregressive Moving Average.

In engineering and technical fields, a large number of sensors are applied to monitor a complex system. A special class of signals are often captured by those sensors. Although they often have indirect or indistinct relationships among them, they simultaneously reflect the operating states of the whole system.