Synchronistical localization, separation, and reconstruction for multiple sound sources are usually necessary in various situations, such as in conference rooms, living rooms, and supermarkets. To improve the intelligibility of speech signals, the application of deep neural networks (DNNs) has achieved considerable success in the area of time-domain signal separation and reconstruction. In this paper, we propose a hybrid microphone array signal processing approach for the nearfield scenario that combines the beamforming technique and DNN. Using this method, the challenge of identifying both the sound source location and content can be overcome. Moreover, the use of a sequenced virtual sound field reconstruction process enables the proposed approach to be quite suitable for a sound field which contains a dominant, stronger sound source and masked, weaker sound sources. Using this strategy, all traceable, mainly sound, sources can be discovered by loops in a given sound field. The operational duration and accuracy of localization are further improved by substituting the broadband weighted multiple signal classification (BW-MUSIC) method for the conventional delay-and-sum (DAS) beamforming algorithm. The effectiveness of the proposed method for localizing and reconstructing speech signals was validated by simulations and experiments with promising results. The localization results were accurate, while the similarity and correlation between the reconstructed and original signals was high.

Constraint by the physical geometry, the lower and upper frequency bound and the scale of the scanning area of a microphone array are limited. Owing to its movable feature, for the service robots, achieving a wider working frequency range with a global view requires a virtually larger and denser array, which can be realised using non-synchronous measurements beamforming with a movable microphone array prototype. However, even when using the state-of-the-art method, it is challenging to localise multiple broadband sources, owing to the difficulty in selecting an appropriate operating frequency without any prior information about the target signal. Therefore, this letter proposes a tensor-completion-based non-synchronous measurements method for broadband multiple-sound-source localisation. The tensor data structure of the broadband signal is analysed, and an alternating direction method based on multiplier optimisation with a tensor multi-norm constraint is proposed. This algorithm can provide a sound map with a distinct global view of three different speech signal sources with high accuracy. Compared with the matrix-based optimisation method, the proposed method can significantly reduce the mean square error of the estimated source location.

As the number of debris increases, the space environment becomes crowded and the risk of collision increases. The active debris removal (ADR) technology is an effective measure to suppress the growth of space debris and stabilize the space environment at a safe level. The ADR based a parent spacecraft carrying multi-sub-satellite is one of the most popular methods to de-orbit space debris. The sub-satellites are equipped with different de-orbit devices to capture the debris and move them to lower orbits. One of the key issues have to deal with is that how to plan an efficient strategy for these sub-satellites to visit all the identified debris in one mission at the lowest weighted sum of energy and time. In order to solve the problem, we model the multi-sub-satellite path planning into a multiple traveler salesmen problem (MTSP). Moreover, we propose an improved multi-sub-population parallel genetic algorithm named Route-Break chromosome pairing, it uses an integer coding method and a new crossover-mutation strategy called single chromosome transformation. Each sub-population evolves independently. Finally, we carry out experiments to testify the efficiency of the proposed algorithm. From the experimental results, we can see that the proposed method has a fast convergence speed and a better solution.

To improve the curve driving stability and safety under critical maneuvers for four-wheel-independent drive autonomous electric vehicles, a three-stage direct yaw moment control (DYC) strategy design procedure is proposed in this work. The first stage conducts the modeling of the tire nonlinear mechanical properties, i.e. the coupling relationship between the tire longitudinal force and the tire lateral force, which is crucial for the DYC strategy design, in the STI (Systems Technologies Inc.) form based on experimental data. On this basis, a 7-DOF vehicle dynamics model is established and the direct yaw moment calculation problem of the four-wheel-independent drive autonomous electric vehicle is solved through the nonsingular fast terminal sliding mode (NFTSM) control method, thus the optimal direct yaw moment can be obtained. To achieve this direct yaw moment, an optimal allocation problem of the tire forces is further solved by using the trust-region interior-point method, which can effectively guarantee the solving efficiency of complex optimization problem like the tire driving and braking forces allocation of four wheels in this work. Finally, the effectiveness of the DYC strategy proposed for the autonomous electric vehicles is verified through the CarSim-Simulink co-simulation results.

Non-synchronous measurement (NSM) is able to improve the spatial resolution of beamforming and the localization accuracy at the cost of longer recording time. This trade-off can be tolerated in various applications of sound source localization (SSL), such as machine fault diagnosis. Therefore, the NSM is a promising localization technique. For stationary acoustic field, the state-of-the-art NSM methods can well localize the stationary sources. However, when the sound sources are non-stationary, these conventional NSM algorithms fail to work effectively. In this work, we propose deep-learning-aided NSM (DL-NSM) to improve non-stationary source localization. Similar to the conventional NSM methods, the DL-NSM utilizes the diagonal blocks of an incomplete covariance matrix of the NSM to determine the non-diagonal elements of this matrix, but differs in the fact that the DL-NSM can achieve the approximation to the covariance matrix of the corresponding synchronous measurement (SM). In the corresponding SM, a prototype array with larger size and denser array element is utilized to localize the sound sources, i.e., the covariance matrix of the corresponding SM can give the correlations of all array elements. This nonlinear approximation of the covariance matrix in the proposed DL-NSM can be learned by the proper training procedure. Therefore, the correlations of the NSM could be recovered, and the array aperture might be expanded, providing high-resolution localization. The simulation and experiment validate the superiority of the proposed DL-NSM.

CrCuFeNi2Tix high-entropy alloys (HEAs) (x = 0.1 ~ 0.7) are prepared and studied in this paper to investigate the effect of titanium on the microstructure, phase composition, and mechanical properties of the CrCuFeNi2Tix-based system. Microstructural studies using scanning electron microscopy (SEM) and X-ray diffraction (XRD) showed that the addition of titanium could induce the formation of a body-centered cubic lattice (BCC) and intermetallic compounds (Ni3Ti) of the CrCuFeNi2Tix-based system. The practical formation of the phases meet the theory of the atomic size difference δ, mixing enthalpy ΔHmix, mixing entropy ΔSmix, valence electron concentration (VEC), and electronegativity difference Δχ. Additionally, the tensile and hardness properties of the CrCuFeNi2Tix-based system are investigated in this study. Generally, CrCuFeNi2Tix HEAs show low stiffness and good flexibility in mechanical properties. When the x value is relatively small, the HEAs show good ductility in the tensile test, which is the result of a face-centered cubic lattice (FCC) in the phase composition at this stage; when the x value becomes larger, due to the formation of the intermetallic compounds Ni3Ti, the HEAs show high hardness