As a subject with strong application, interior design is intended to guide students to use design concepts and technical means scientifically according to the nature and standards of architecture, so ...as to create the indoor environment that people need. With the innovation of Internet technology and the rapid development of innovation and entrepreneurship, the integration of innovation and entrepreneurship education and interior design education in higher vocational buildings has become an inevitable trend of talent training. However, at present, there are still some problems in the innovation and entrepreneurship education of interior design major in China, such as the imperfect curriculum system, the insufficient degree of integration of curriculum and specialty, the weak spirit of innovation and entrepreneurship of higher vocational students, the insufficient ability of innovation practice and the lack of practice. In view of this, on the basis of experience at home and abroad, this paper tries to put forward the integration way of innovative entrepreneurship education and interior design professional education from the aspects of the reform of talent training mode, the innovation of curriculum system, the construction of practical platform, and the construction of teaching staff, in order to achieve better educational effect.
The cache-enabling unmanned aerial vehicle (UAV) non-orthogonal multiple access (NOMA) networks for mixture of augmented reality (AR) and normal multimedia applications are investigated, which is ...assisted by UAV base stations. The user association, power allocation of NOMA, deployment of UAVs and caching placement of UAVs are jointly optimized to minimize the content delivery delay. A branch and bound (BaB) based algorithm is proposed to obtain the per-slot optimization. To cope with the dynamic content requests and mobility of users in practical scenarios, the original optimization problem is transformed to a Stackelberg game. Specifically, the game is decomposed into a leader level user association sub-problem and a number of power allocation, UAV deployment and caching placement follower level sub-problems. The long-term minimization was further solved by a deep reinforcement learning (DRL) based algorithm. Simulation result shows that the content delivery delay of the proposed BaB based algorithm is much lower than benchmark algorithms, as the optimal solution in each time slot is achieved. Meanwhile, the proposed DRL based algorithm achieves a relatively low long-term content delivery delay in the dynamic environment with lower computation complexity than BaB based algorithm.
The air-ground cooperative emergency networks can assist with the rapid reconstruction of communication in the disaster area, where unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) ...are deployed as base stations. The trajectory optimization of emergency base stations is of vital importance to the communication performance, which is related to the timeliness and effectiveness of rescue. In this paper, federated multi-agent deep deterministic policy gradient (F-MADDPG) based trajectory optimization algorithm is proposed to maximize the average spectrum efficiency. Specifically, the property of MADDPG is inherited to jointly control of multiple vehicles and federated averaging (FA) is utilized to eliminate the isolation of data to accelerate the convergence. Distributed F-MADDPG (DF-MADDPG) is further designed to reduce the communication overhead with a distributed architecture. The simulation results indicate that the proposed F-MADDPG and DF-MADDPG based algorithms significantly outperform the existing trajectory optimization algorithms, in terms of the average spectrum efficiency and the speed of convergence.
Impact loading on carbon fiber reinforced polymer matrix (CFRP) composite laminates can result in a significant reduction in their residual properties, and the (ShAI) properties of the composite ...material are essential to obtain the material allowable values of the shear dominated composite structures. In order to obtain the ShAI properties of the composite material in pure shear stress at a coupon level, this study presents theoretical, experimental, and numerical methods and analysis work on the in-plane shear and ShAI properties of the composite laminates. Theoretically, a method of sizing the composite specimen loading in shear is developed through comparing the load values due to buckling and the material failure. Following this, both impact tests using the drop-weight method and ShAI tests using the picture frame test method are conducted, and the influences of the impact energies on the impact damage and the residual ShAI values are evaluated. Moreover, a progressive failure finite element model based on the Hashin’s failure criterion and the cohesive zone model is developed, and a two-step dynamic analysis method is performed to simulate the failure process of the composite laminates under impact loading and ShAI loading. It is found that the impact damage with the cut-off energy, 50 J, causes a 26.8% reduction in the residual strength and the residual effective shear failure strain is about 0.0132. The primary reason of the shear failure is the propagation of both the matrix tensile failure and interlaminar delamination. It can be concluded that the proposed theoretical, experimental, and numerical methods are promising factors to study the ShAI properties of the composite materials.
In this paper, the non-orthogonal multiple access (NOMA) technology is integrated into cognitive orthogonal frequency-division multiplexing (OFDM) systems, called cognitive OFDM-NOMA, to boost the ...system capacity. First, a capacity maximization problem is considered in half-duplex cognitive OFDM-NOMA systems with two accessible users on each subcarrier. Due to the intractability of the considered problem, we decompose it into three subproblems, i.e., the optimization of, respectively, sensing duration, user scheduling, and power allocation. By investigating and exploiting the characteristics of each subproblem, the optimal sensing duration adaptation, a matching-theory-based user scheduling, and the optimal power allocation are proposed correspondingly. An alternate iteration framework is further proposed to jointly optimize these three subproblems, with its convergence proved. Moreover, based on the non-cooperative game theory, a generalized power allocation algorithm is proposed and then used in the framework to accommodate half-duplex cognitive OFDM-NOMA systems with multiple users on each subcarrier. Finally, the proposed framework is extended to solve the capacity maximization problem in full-duplex cognitive OFDM-NOMA systems. Simulation results validate the superior performance of the proposed algorithms. For example, for the case of two accessible users, the proposed framework approaches the optimal solution with less than 1% capacity loss and 120 times lower complexity compared with exhaustive search.
Recently, electrides have received increasing attention due to their multifunctional properties as superconducting, catalytic, insulating, and electrode materials, with potential to offer other ...performance and possess novel physical states. This work uncovers that Li5N as an electride possess four novel physical states simultaneously: electride state, super-coordinated state, superconducting state, and superionic state. By obtaining high-pressure phase diagrams of the Li–N system at 150–350 GPa using a crystal structure search algorithm, we find that Li5N can remain stable as P6/mmm structure and has a 14-fold super-coordination number, as verified by Bader charge and electron localization function analysis. Its superconducting transition temperature reaches the highest at 150 GPa (Tc = 48.97 K). Besides, Li5N exhibits the superionic state at 3000 K, in which N atoms act like solid, while some Li atoms flow like liquid. The above results are further verified at a macroscopic level by using deep learning potential molecular dynamics simulations.
•A sensorless and adaptive admittance control of an industrial robot is proposed for physical human−robot interaction.•A comprehensive dynamic model of the industrial robot is obtained to enable the ...external force detection.•The dynamics of robot in the process of mode switching is explored and compensated effectively.•RBF network is used to tune the admittance controller online to make human−robot interaction more natural and easier.
As industrial robots are applied in manufacturing industry on a large-scale and human intelligence is regarded as an important part in manufacturing, physical human−robot interaction (pHRI) which integrates the strength and accuracy of robot with human operator's ability of task cognition has drawn the attention of both academia and industry. However, an industrial robot without extra force/torque sensor for interacting force monitoring cannot be used directly in pHRI, and research on pHRI of industrial robots remains a challenge. In this research, a comprehensive dynamic model of an industrial robot in both dynamic mode and quasi-static mode is obtained to calculate the external force produced by human operator in pHRI and enables sensorless pHRI for industrial robots even in the environment with ambient vibration. Particularly, the dynamics in the process of mode switching which has not been investigated by researchers is studied and compensated by an empirical but effective method. Admittance control is used to transfer the detected force into reference position and velocity of the robot. RBF (Radial Basis Function) network is used to update the damping parameter online in order to reduce the contact force change and the contact force which makes pHRI more natural and easier. The stability of the controller is also discussed. The proposed methods of external force detection and adaptive admittance control show satisfactory behaviour in the experiments.
Assembly is a critical step in the manufacturing process. Robotic assembly technology in automatic production lines has greatly improved the production efficiency. However, in assembly process, ...dynamic disturbances such as processing time change and advance delivery may occur, which cause the scheduling deviation. Traditional scheduling methods are not sufficient to meet the real-time and adaptive requirements in smart manufacturing. Digital twin (DT) has the characteristics of virtual-reality interaction and real-time mapping. In this paper, we propose a DT-based framework of task rescheduling for robotic assembly line (RAL) and its key methodologies, thus to realize the timely and dynamic adjustment of scheduling plan under uncertain interferences. First, a DT model of RAL task rescheduling composed of physical entity (PE), virtual entity (VE), and virtual-reality interaction mechanism is proposed. Then, a mathematical model is established. By analyzing the adaptive objective thresholds from the perspectives of event trigger and user demand trigger, a DT-driven multi-level (production unit level and line level) rescheduling strategy is proposed. Taking both the computing time and solution quality into consideration, the precedence graph is introduced to propose a rescheduling approach based on an improved discrete fireworks algorithm. Finally, the effectiveness of the proposed model and approach are verified by task scheduling experiments of RAL.
Moving shadow elimination plays an important role in the field of moving object detection. However, the accuracy of shadow elimination is an open question, due to illumination and complex texture. ...Furthermore, the problem of misclassification of moving object caused by shadow has also become increasingly serious. To address this problem, this paper presents a moving shadow elimination algorithm based on the fusion of multi-feature pattern, which can enhance the accuracy of moving object detection system. In this approach, a dual-channel HSV color space feature and a uniform extended scale invariant local ternary pattern (UESILTP) texture feature are synthesized to elimination shadow. It greatly overcomes the misjudgment of dark object by color feature and the false detection caused by inconspicuous texture characteristics of moving object. Meantime, a method of region growth is adopted to fill the existing cavities in the color space. Finally, qualitative and quantitative comparisons with state-of-the-art methods show that the algorithm is effective.
In this paper, to enhance the spectrum utilization in cognitive unmanned aerial vehicle networks (CUAVNs), we propose a cooperative spectrum sensing scheme based on a continuous hidden Markov model ...(CHMM) with a novel signal-to-noise ratio (SNR) estimation method. First, to exploit the Markov property in the spectrum state, we model the spectrum states and the corresponding fusion values as a hidden Markov model. A spectrum prediction is obtained by combining the parameters of CHMM and a preliminary sensing result (obtained from a clustered heterogeneous two-stage-fusion scheme), and this prediction can further guide the sensing detection procedure. Then, we analyze the detection performance of the proposed scheme by deriving its closed-formed expressions. Furthermore, considering imperfect SNR estimation in practical applications, we design a novel SNR estimation scheme which is inspired by the reconstruction of the signal on graphs to enhance the proposed CHMM-based sensing scheme with practical SNR estimation. Simulation results demonstrate the proposed CHMM-based cooperative spectrum sensing scheme outperforms the ones without CHMM, and the CHMM-based sensing scheme with the proposed SNR estimator can outperform the existing algorithm considerably.
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