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Stop-and-go traffic poses significant challenges to the efficiency and safety of traffic operations, and its impacts and working mechanism have attracted much attention. Recent studies have shown that Connected and Automated Vehicles (CAVs) with carefully designed longitudinal control have the potential to dampen the stop-and-go wave based on simulated vehicle trajectories. In this study, Deep Reinforcement Learning (DRL) is adopted to control the longitudinal behavior of CAVs and real-world vehicle trajectory data is utilized to train the DRL controller. It considers a Human-Driven (HD) vehicle tailed by a CAV, which are then followed by a platoon of HD vehicles. Such an experimental design is to test how the CAV can help to dampen the stop-and-go wave generated by the lead HD vehicle and contribute to smoothing the following HD vehicles’ speed profiles. The DRL control is trained using real-world vehicle trajectories, and eventually evaluated using SUMO simulation. The results show that the DRL control decreases the speed oscillation of the CAV by 54% and 8%-28% for those following HD vehicles. Significant fuel consumption savings are also observed. Additionally, the results suggest that CAVs may act as a traffic stabilizer if they choose to behave slightly altruistically.

This paper reports the method for constructing multiple coexisting attractors from a chaotic system. First, a new four-dimensional chaotic system with only one equilibrium and two coexisting strange attractors is established. By using bifurcation diagrams and Lyapunov exponents, the dynamical evolution of the new system is presented. Second, a feasible and effective method is applied to construct an infinite number of coexisting attractors from the new system. The core of this method is to batch replicate the attractor of the system in phase space via generating multiple invariant sets and the generation of invariant sets depends on the equilibria, which can be extended by using some simple functions with multiple zeros. Finally, we give some numerical results of the appearance of multiple coexisting attractors in the system with sine and sign functions for demonstrating the effectiveness of the method.

In this paper, we propose a novel scheme for physical-layer network coding (PNC) in multiway relay channels (MWRC) from the perspective of sequential multiuser detection. We consider an uplink MWRC scenario where N users, each equipped with a single antenna, simultaneously transmit their signal to a relay equipped with K antennas (K

In recent years, the service robot has been developed rapidly, which has brought a lot of convenience for industries and human life. It is of great significance to study service robot. Mission planning is the key component of robot, which is used to solve the sequential decision problem of robot. Because of the highly dynamic and uncertain working environment of service robot, how to model and deal with these complex environments is very important for agents to complete mission planning correctly and efficiently. In this paper, a multimission planning method based on L-HMM and utility function description considering path cost for service robot are proposed, high-dimensional partially observable states of agents are abstractly classified into three-dimensional states, combined with HMM model based on Bayesian network. L-HMM not only reasonably models the stochastic process of agent, but also avoids the dimension explosion problem in the process of model solving. In addition, in the iterative process of utility function, considering the impact of path cost on planning effect prevents the agent from planning irrationally and inefficiently due to the encapsulation of navigation nodes. Finally, humanoid robot NAO is used to verify the effect of the theory proposed, multi-mission experiments show the planning results are excellent and highefficient.

In this paper, we propose a novel power allocation scheme for physical-layer network coding (PNC) in downlink multi-way relay channels (MWRC). The power allocation is formulated as a constrained optimization problem, where the aim is to maximize the success probability under a total power constraint when using Babai estimation for signal detection. Optimizing over this metric allows us to maximize the probability of successfully decoding a chain of network codes, which is of crucial importance in downlink multi-way PNC. Specifically, to meet the different requirements for transmission quality in applications, we consider different aggregate measures of success probability over the participating user terminals, i.e., the arithmetic mean, the geometric mean, and the maximin. For each measure, we formulate a constrained optimization and demonstrate the concavity of the objective, allowing us to obtain solution efficiently via iterative means. The performance of the proposed power allocation schemes for downlink PNC in MWRC is evaluated by means of computer simulations over Raylegih fading channels. The results demonstrate the effectiveness of the proposed schemes in improving the success probability in the reception of a chain of network codes.

This paper takes the influence of tax collection and management efficiency on enterprise tax avoidance behavior as the research object, and discusses the influence mechanism of macro policy at the government level on micro enterprise behavior. First of all, this paper uses the three-stage DEA model to measure the tax collection and management efficiency of 28 provinces in China in 2017. Secondly, the multiple regression model is used to empirically analyze the impact of tax collection and management efficiency on corporate tax avoidance activities. It is found that the efficiency of tax collection and management has a significant impact on corporate tax avoidance behavior, and every 1 percentage point increase in tax collection and management efficiency will increase the tendency of corporate tax avoidance by 0.04 percentage points. And local financial pressure will negatively regulate the impact of tax collection and management efficiency on tax avoidance.

There has been a growing interest in compressed wideband spectrum sensing. One significant challenge in this field is how to effectively overcome the sparsity dependence. To solve this, two adaptive sparsity methods based on one-bit compressed sensing are explored for wideband spectrum sensing. One method (ASBIHT) is proposed by introducing adaptive sparsity into the binary iterative hard threshold method (BIHT). Using the magnitude of energy, it can accurately reconstruct the signals with unknown sparsity by learning the signal and noise. The other method is constructed by combining the binary iterative hard threshold algorithm with the pinball loss function (PIHT), and adaptive sparsity (ASPIHT). Utilizing the fixed step, the hard threshold parameter is adjusted to approach the signal sparsity gradually. Simulation results verify the efficacies of two proposed methods in the context of wideband spectrum sensing.

In order to establish an accelerated life test method for thick film hybrid integrated circuits (HIC) and obtain its lifetime data, the accelerated life experiment of medium power thick film HIC was carried out, and the experiment results were summarized and analyzed. Based on the analysis of influencing factors of thick film HIC reliability, and the time-temperature regression in table 1 of mil-std-883h method 1015.10, three groups of comparative experiments were conducted in combination with the characteristics of the product. Group 1 is the test requirement in the detailed specification and is the comparison benchmark between group 2 and group 3.The difference between group 2 and group 3 is the different load. After high temperature burn in test, the degradation of thick film HIC would be accelerated and the sample failure would be caused. Failure analysis showed that high temperature burn in test can induce Au4A1 and in Kirkendall void in Au/A1 bonding system, in the following steady state life experiment, with the continuous production of Au4A1 and the Kirkendall void expanding, bonding wire contact resistance increases, eventually the bonding wire of triode V2 in the sample produced strength failure, caused PWM lose power supply, which made the sample output failure. All these fail samples are in the same group, it implys that worst experiment condition can speed up the degradation of thick film HIC, thus it is recommend that the case temperature should not exceed 125°C during the operation condition.

Estimating the integer parameter vector in a linear model with additive Gaussian noise arises from many applications, including communications. The optimal approach is to solve an integer least squares (ILS) problem, which is unfortunately NP-hard. Recently Klein’s randomized algorithm, which finds a sub-optimal solution to the ILS problem, to be referred to as the randomized Babai point, has attracted much attention. This paper presents a formula of the success probability of the randomized Babai point and some interesting properties, and compares it with the deterministic Babai point.

For multi-target imaging task, the time to image all of the targets is very limited, and it would be best to use less time to complete each imaging task. Although the radar network which is constituted by multiple independent and distributed radars can increase efficiency and shorten the observation time for multi-target imaging by the division of labor and cooperation, it is necessary for the radar network to design a multi-target imaging task allocation strategy to optimize the observation time of each radar and improve the time resource utilization. However, there is few study focusing on the imaging task allocation problem. Task allocation problem about multi-target inverse synthetic aperture radar (ISAR) imaging in radar network is a distributed decision problem, thus the game theory could be taken into account to solve the task allocation problem. In this paper, the system model about multi-target imaging and the imaging task allocation problem are given firstly. Moreover, a time optimization game model for multi-target imaging task allocation problem is proposed, and the game model is proven to be a potential game model which has at least one pure-strategy Nash equilibrium (NE). Finally, the best-response dynamics is applied to achieve the solution of the imaging task allocation problem, and the convergence of the time optimization is given to verify that the multi-target imaging task allocation problem can be constructed as a potential game model.

Traditional 2-D inverse synthetic aperture radar (ISAR) imaging can be regard as an inverse problem to reconstruct the target's scattering distribution from the echo. However, 2-D ISAR cannot represents the 3-D geometric relationship between the radar and targets, so it can only obtain the target's scattering projection on the corresponding Range-Doppler plane rather than the target's 3-D scattering distribution. To constitute a radar network, several radars should be selected from the all available radars. Because the distribution of the selected radars make an impact on the imaging performance, the distribution optimization should be considered in radar network imaging. In this paper, a novel 3-D ISAR image reconstruction method based on the optimized radar network is proposed. Firstly, the 3-D ISAR image reconstruction model is constructed by combining the projection relationship of each ISAR image and the distribution optimization. Secondly, the corresponding algorithm is proposed. With the proposed method, the optimal radar network distribution and the 3-D ISAR image can be obtained effectively. Finally, experiments demonstrate the performance of the proposed method.

Many current analyses on protein-protein interaction (PPI) networks concentrate on developing the algorithms for identifying the functional modules within the PPI networks. However, understanding the internal structure in the functional modules is needed to define their roles within the entire network. We propose the use of a two-club meso-scale structure that possesses refined inner topological structural properties, useful for deciphering the aforementioned obscure structural supporting evidences. In this paper, we: 1) illustrate the feasibility and advantages of modeling functional modules as two-clubs in PPI networks by taking statistics on the diameter distribution of benchmark functional modules within several golden standard sets; 2) categorize the two-clubs into six subcategories through the use of well-defined internal graph-theoretic characterizations; and 3) analyze these six subcategories based on factors, such as their structure, and various metrics, such as topological centralities, the numbers of involved transcription factors and essential genes, and the numbers of matched protein complexes or GO terms. Our structure-driven analysis allows us to predict the roles of identified functional modules from their network structure alone. Our subcategories of coteries and social circles serve as a classification scheme for determining which functional modules are central, regional, and satellite and which are coordinating. Experimental results show that in order to precisely control and examine the PPI networks for further research, a clear understanding of how the internal topology of modules affects their function is essential.