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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.

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.

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.

The transfer learning between the source and target domain has already achieved significant success in machine learning areas. However, the existing methods can not achieve satisfactory result when solving the two distant domains transfer learning problem. In the worst case, it could lead to the negative transfer. In this paper, we propose a novel framework called transitive transfer sparse coding (TTSC) to solve the two distant domains transfer learning problem. On the one hand, as an extension of the sparse coding, the TTSC framework constructs a robust and high-level dictionary across three different domains and simultaneously obtains three good feature sparse representations. On the other hand, TTSC utilizes the intermediate domain as a strong bridge to transfer valuable knowledge between the source domain and target domain. Empirical studies validated that the TTSC framework significantly could outperform state-of-the-art methods.

Object detection algorithms play an important role in the field of violation detection. However, small target detection is full of challenges in scenes related to human. The relationship between objects is usually not considered in object detection algorithms, which will make the model over relay on the high-order features and not make full use of local features. To address this issue, a novel framework named PointDet is proposed to learn local features which optimizes the detection effect of small targets in chemical plants. Since most of the targets to be detected in our dataset are highly correlated with human, human local features are used when designing the framework. First, we use a trained pose estimation model to extract local key point features. However, if local features are used directly, the relationship between them cannot be fully considered. Based on this situation, we have designed the one-vs-others module and the adaptive-graph-convolution module to reconstruct local features. In addition, for the output layer, the most challenging problem is how to better detect small targets. In our task, various small objects such as gloves, goggles, etc. have an obvious positional relationship with the local features of human body. In the output layer, we have designed a head attention module to make full use of this situation to optimize the small target detection problem. Specifically, our framework significantly outperforms state-of-the-art by 7.8 AP scores on field work dataset in chemical plants.

Virtual reality (VR) holds a great potential to provide interactive and immersive learning experiences for students in remote education by using existing mobile devices, which is extremely meaningful during the current pandemic. In such a VR application, satisfactory user experience requires: 1) high-resolution panoramic image rendering; 2) high frame rate; 3) synchronization among users. This requires that either mobile devices perform fast image rendering or today’s wireless network can support multi-Gbps traffic with extremely low delay, neither of which is the case in current practice. In this demo, we develop a platform for interactive and immersive remote education based on commodity devices, where a server performs rendering to ensure that the rendered images have high-resolution (2560×1440 pixels) and are displayed at a high frame rate (60 frames per second) on the client-side. We further leverage motion prediction to overcome the diverse round-trip time (RTT) between a server and users and ensure synchronization among users (average 9.2 ms frame latency difference among users), which improves at least 60% and 20% compared to the existing local-rendering and server-rendering methods, respectively.

In this paper, a Substrate Integrated Coaxial Line (SICL) structure is designed to the mmWave antenna with multilayer configurations for eliminating the high-order interferences. The antenna operates in the first zero-mode generated by shorting pins, hence, to achieve a miniaturized size with 0.15 $\lambda_{0} \times 0.21 \lambda_{0}$ ($\lambda_{0}$ is the free-space wavelength of 28 GHz). However, the multi-layer configurations induce interferences to the mmWave antenna, particularly generates high-mode resonances. The proposed SICL design containing eight via holes resembles the transmission line structures in the substrate, thus, mitigate these interferences and maintain the S 11 parameter the same as that of original two-layer design. Moreover, using the miniaturized microstrip patch as antenna element, a $4 \times 1$ array is also constructed and discussed. Good operating performance including 7.2 dBi realized gain and 0.8 GHz impedance bandwidth is obtained, which indicates that the proposed antenna has the advantages of smaller size and potential of assembling array

With the rapid development of big data and cloud computing, the analysis and prediction of user loan repayment behavior has become a research hotspot in the field of financial technology. The majority of previous studies on such issues have focused on e-commerce platforms and commercial banks, but we innovatively apply the data mining and machine learning methods to the provident fund users. In addition to the traditional two prediction classes of normal loan repayment and loan overdue, we have also added the early loan repayment class to help the provident fund center better manage asset liquidity. We proposed a model based on stacking ensemble method, and compared with six individual models: Cart decision tree, Random Forest, AdaBoost, XGBoost, CatBoost and LightGBM. After experiments, our stacking model has a good improvement under the classification performance evaluation metrics such as accuracy score, recall score, F1 score, AUC score, and kappa score. Furthermore, we also analyzed the importance of features in the process of training the loan repayment behavior prediction model. Through our stacking model and research analysis, the provident fund centers can reduce loan risks and provide better services to their customers.

In this paper, a three-step finite element model for accurate simulation of Ultrasonic Peening Forming (UPF) process is proposed with the computer assisted analyses. First, an explicit FEA model was built with ABAQUS to simulate the impact process, in which the distributions of resulting residual stress inside the simplified sub-model could be obtained. Then, by using a C language program, the resulting stress data for centre-impact zone of the sub-model was extended to the full-size complete target plate. Finally, the arc height and the curvature radii of the new target plate in transverse and longitudinal directions are calculated by an implicit FEA model in ABAQUS. Numerical results of the peening process reveal the influence of the different controlled parameters on the residual stress profiles as well as the resultant curvature deformation of target plate. The experiment related to the thickness of the plate is carried out. This numerical simulation impact model could be used for predicting the residual stress distribution and panel deformation, providing another alternative for investigating and optimizing the UPF process.

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.

With the rapid development of cities, massive and complex traffic data is being generated and collected. The traffic data is not intuitive and cannot highlight key information about urban traffic conditions. However, traffic data visualization can directly correlate users with the data, and support users to interact with data in a convenient and visual way. Then realize the feedback of blending user wisdom and machine intelligence. This paper investigates a structured survey of the state of the art in the visualization of traffic data. First, we reviewed five representative traffic data visualization methods including WebVRGIS based traffic analysis and visualization system, TripMiner, IoV distributed architecture, SMASH architecture, and LDA-based topic modelling. Meanwhile, we analyzed the traffic datasets that applied in each method. Then we summarize these methods from seven aspects: scalability, data storage, data update, interactivity, reliability, data anomaly detection, and spatiotemporal visualization. In addition, we make a detailed comparative analysis of the key capabilities of five representative traffic data visualization methods in processing traffic big data. Finally, we conclude that the SMASH architecture performs better in processing high speed and large flow traffic data. Moreover, we propose a novel direction for optimizing traffic data visualization techniques.

Association rule is an important analysis method of data mining, combined with the particularity of energy industry, this paper applies association rule to the analysis of regional energy consumption. The energy consumption and its influencing factors in 30 regions (provinces/autonomous regions/municipalities directly under the central government) in China are mined and analyzed by using the method of association rules comprehensive mining based on FP-growth algorithm. The association rules between energy consumption and regional population scale, regional climate, regional GDP and its growth rate, the proportion of regional secondary industry, regional per capita GDP and other factors are obtained. The results are of high reference value, quantitative association rules can predict the trend of regional energy consumption growth. The analysis of influencing factors can provide theoretical reference for regional energy conservation development.