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Land mobile satellite communication and physical layer security are considered as the promising paradigms in beyond 5G networks. In this paper, we establish the multi-relay hybrid satellite-terrestrial systems (HSTSs) with artificial noise (AN) to investigate the reliability and security, in which the satellite sends signals to the terrestrial destination through multiple decode-and-forward relays and there exists an eavesdropper trying to overhear the useful information. In order to improve the overall system performance, two relay selection (RS) schemes: i) optimal relay-receiver pair selection (ORRPS) and ii) suboptimal relay selection (SRS) are considered. ORRPS selects an optimal relay-receiver pair and an AN signal to maximize the corresponding signal-to-interference-plus-noise ratios of ${R_{k}} \to D\left ({{R_{k} \to E} }\right)$ and the SRS refers to selecting a relay that optimize the instantaneous channel gain ${\left |{ {{h_{R_{k}D}}} }\right |^{2}}\left ({{{{\left |{ {{h_{R_{k}E}}} }\right |}^{2}}} }\right)$ among $K$ relays, with the traditional round-robin selection scheme as a benchmark for comparison. Furthermore, the exact closed-form analytical expressions of outage probability (OP) and intercept probability (IP) under different RS schemes are derived for AN and non-AN conditions. To obtain more insights, the asymptotic analysis for high signal-to-noise-ratio regime is carried out. Based on the asymptotic analysis of OP, the diversity orders of the HSTSs are obtained. The numerical results and theoretical analysis shown that: i) The reliability of TRRS scheme is the worst among the three RS schemes, and the reliability of ORRPS scheme is the best; ii) The AN technology can significantly improve the security of the system, although it has a little adverse affect on the reliability; iii) Infrequent light shadowing has the best reliability, frequent heavy shadowing has the worst outage performance, and the change of security is opposite; iv) The OP of SRS and ORRP schemes decreased with the increase of $K$ , while the IP changed on the contrary, and the reliability and security of TRRS scheme did not change obviously.

Urea line heating system is critical for Selective Catalytic Reduction (SCR) aftertreatment system under low temperature environment. Traditionally, the Electronic Control Unit (ECU) motivates urea lines heating command and diagnostics directly. This paper illustrates a Controller Area Network (CAN)-based line heater architecture development for the SCR aftertreatment system, as well as the diagnostics generation. The CAN-based line heater diagnostics is validated on vehicle experimental with four different given cases. It can be seen from the validation results that the diagnostics logic and algorithm are reasonable, and the line heater failure modes could be detected correctly including open circuit, short circuit and inducement. In addition, the failure modes are able to set or clear for each of the urea lines according to the diagnostics logic. ECU and ECU1 could communicate well between each other by CAN. This study introduces a new line heater architecture for SCR aftertreatment system which is meaningful for the whole vehicle assembly, which could be considered in the real application in future.

In the context of failure control of servo system driven by twin motors, there are still no available results to compensate unknown actuator failures which seem inevitable in practice by adaptive backstepping technique. Therefore, to rise the reliability of the system, we aim at addressing such a problem by proposing an adaptive robust actuator failure compensation control scheme based on backstepping technique for servo system driven by twin motors. Unlike the traditional backstepping, the estimation of unknown coefficient of intermediate state variable is introduced in coordinate changes. In addition, matching and non-matching uncertainties have been fully considered in the controller design. Simulation results show that the designed controller can ensure the boundedness of all the signals no matter actuator failures occur or not.

Tensor sparse coding (TSC) is a method used to excavate 3D volume structures extended by sparse coding (SC), which is increasingly applied in data noise attenuation. Existing TSC approaches control the intensity of noise attenuation by using a predetermined soft or hard threshold that relies on the noise variance. However, the noise variance in seismic data is unknown and varies with time and space, leading to the conventional TSC method not being able to track this change. To address this issue, we proposed a tensor sparse coding model with spatially adaptive coherent constraint (TSC-SAC) to find an optimal adjustable threshold without the demand for prior knowledge of the noise variance. The threshold is determined by the coherence of the residual with respect to the dictionary. Moreover, a tensor spatial coherence orthogonal matching pursuit algorithm (TSC-OMP) is developed for solving sparse representation. Unlike the existing threshold strategy in traditional TSC methods, TSC-OMP utilizes an ideal spatially adaptive coherence threshold to regulate the sparsity, which can effectively preserve the valuable information in processing for noise suppression. By comparing with four state-of-the-art denoising algorithms, we then demonstrated the superior performance of TSC-SAC on both a synthetic and two field data sets.

In this paper, an adaptive backstepping control scheme is proposed for a class of nonlinear systems with unknown time delay. The unknown time delay in the system depends on all system states, which will inevitably destroy the triangular structure of the control system. Therefore, traditional backstepping can not be used to design the controller. In order to overcome the problem caused by “non-triangular structure”, we construct a linear projection between the state vector and its transform in controller design. Using inequality and adaptive backstepping control, we compensate the uncertainties caused by unknown time delays. Finally, the simulation results show that the proposed algorithm is effective.

Pneumatic muscle is a relatively new pneumatic component in some precise and flexible control systems and such systems are needed to achieve a higher controlling performance. However, the existence of practical uncertainties will make it hard to achieve a higher control accuracy and better performance. In this paper, the adaptive control problem for the pneumatic muscle joint system with input saturation, external disturbance and modeling errors is investigated. The auxiliary signals r1, r2 and function ψsgn as an approximation of sign(·) function is introduced to deal with the unknown saturation and uncertainties caused by modeling errors. Then an adaptive control scheme is proposed by using these auxiliary signals and functions. Finally, simulation studies are used to verify the effectiveness of the proposed control scheme.

For the sake of better balancing the relationship between diversity and convergence when handling constrained optimization problems, a two-stage adaptive constrained particle swarm optimization algorithm based on bi-objective method (TABC-PSO) is proposed. In accordance with different phases of the constraint process, the target-constraint space derived from the angle is partitioned adaptively, and simultaneously the global best particle is selected and the external archive set is safeguarded. In the first stage, the whole space is divided adaptively in term of the angular distribution of individual, and the feasible region is explored comprehensively. In the second stage, local regions are adaptively compartmentalized and in-depth exploitation is carried out. Primary and secondary external archive sets are established to maintain population diversity and accelerate convergence. The two phases are switched adaptively in light of the storage status of the two external archive sets. We evaluated TABC-PSO algorithm on the benchmark functions in CEC 2006 and CEC 2017. The experimental results show that TABC-PSO algorithm compared with other state-of-the-art algorithms can be superior to applied to test functions with different types of constraints and possesses a competitive search capability.

Adaptive backstepping control is an effective method for servo system driven by twin motors with unknown actuator failures, however, problems such as explosion of complexity make it can not be used in the actual control systems. In this paper, an algorithm of adaptive dynamic surface control is proposed for servo system driven by twin motors with unknown actuator failures, which can eliminate the expansion of the differential term and simplify calculations in the controller design. In addition, all parameters and disturbances in the system model are unknown, which require to be identified online. The simulation results also provided in order to show the effectiveness of the algorithm.

It is difficult to balance the convergence and diversity at the same time and select the globally optimal particle (gbest) in the multiobjective particle swarm optimization (MOPSO) process. In this paper, a novel method based on adaptive angle division is proposed targeting at archive maintenance and gbest selection. According to the number of particles in the current archive, the angle region of the target space is adaptively adjusted and uniformly divided. The globally optimal particle is selected from the low-density angle region of the particle distribution; in the meanwhile, the superfluous particles are deleted from the high-density angle region. Consequently, the selection of gbest and update of external archive set which including the generation of the optimal solution set and maintenance of external archive set are synchronized. The convergence and diversity are ensured while improving the uniformity of the optimal solution set. In addition, the current highest non-dominated particles in each angle region are preserved, and the particles in adjacent regions are selected to conduct global guidance for such regions and regions of the particle-free distribution area. Through these two ways, the diversity of the population is maintained while the coverage spreadability of the optimal solution set in the target space is enhanced. Four state-of-the-art evolution multiobjective optimization (EMO) algorithms and the two classic EMO algorithms are selected as the peer algorithms to validate multiobjective particle swarm optimization based on adaptive angle division (AADMOPSO) algorithm. A series of extensive experiments are conducted on five groups of standard test functions. The experimental results show the effectiveness and competitiveness of the proposed AADMOPSO in balancing convergence and diversity.

Imbalanced data exists commonly in machine learning classification applications. Popular classification algorithms are based on the assumption that data in different classes are roughly equally distributed; however, extremely skewed data, with instances from one class taking up most of the dataset, is not exceptional in practice. Thus, performance of algorithm often degrades significantly when encountering skewed data. Mitigating the problem caused by imbalanced data has been an open challenge for years, and previous researches mostly have proposed solutions from the perspectives of data re-sampling and algorithm improvement. In this paper, focusing on two-class imbalanced data, we have proposed a novel divergence-encouraging autoencoder (DEA) to explicitly learn features from both of the two classes and have designed an imbalanced data classification algorithm based on the proposed autoencoder. By encouraging maximization of divergence loss between different classes in the bottleneck layer, the proposed DEA can learn features for both majority and minority classes simultaneously. The training procedure of the proposed autoencoder is to alternately optimize reconstruction and divergence losses. After obtaining the features, we directly compute the cosine distances between the training and testing features and compare the median of distances between classes to perform classification. Experimental results illustrate that our algorithm outperform ordinary and loss-sensitive CNN models both in terms of performance evaluation metrics and convergence properties. To the best of our knowledge, this is the first paper proposed to solve the imbalanced data classification problem from the perspective of explicitly learning representations of different classes simultaneously. In addition, designing of the proposed DEA is also an innovative work, which could improve the performance of imbalanced data classification without data re-sampling and benefit future researches in the field.