Current Issue : January-March Volume : 2024 Issue Number : 1 Articles : 5 Articles
With the development of sharing economy, a joint distribution mode has been increasingly adopted as the preferred cooperation mode of third-party logistics enterprises to achieve the efficient, resource-saving, and profit-optimal business goals of enterprises. In the joint distribution mode, the distribution price is one of key factors that influences the operation of the joint distribution. Thus, to acquire the optimal pricing for the logistics enterprises, we establish a pricing model based on dynamic game theory for a joint distribution system including one joint distribution company and two express enterprises. In the proposed model, two dimensions of games exist simultaneously, including the game between express competitors and the game between express and distribution enterprises. The multidimensional game leads to more complex system characteristics. Through the stability analysis, we find the Nash equilibrium point and its stability conditions. Numerical simulations are conducted to investigate the complex dynamical behaviors of the game model, such as the system stability region, the bifurcation diagram, the largest Lyapunov exponent, strange attractors, etc. The simulation results indicate that different price adjustment speeds and ranges have a significant impact on the system stability and the profits of all participants in the game. The parameter adjustment control can well dominate the chaotic behaviors of the system. Enterprises should make pricing decisions based on their market positions to promote the continuous and stable development of the operation mode of the multi-agent joint sharing distribution center....
With the advancement of virtual reality and 3D game technology, the demand for high-quality 3D indoor scene generation has surged. Addressing this need, this paper presents a method leveraging a VAE-GAN-based framework to conquer two primary challenges in 3D scene representation and deep generative networks. First, we introduce a matrix representation to encode fine-grained object attributes, alongside a complete graph to implicitly capture object spatial relations—effectively encapsulating both local and global scene structures. Second, we devise a unique generative framework based on VAE-GAN and the Bayesian optimization. This framework learns a Gaussian distribution of encoded object attributes through a VAE-GAN network, allowing for sampling and decoding of the distribution to generate new object attributes. Subsequently, a U-Net is employed to learn spatial relations between objects. Lastly, the Bayesian optimization module amalgamates the generated object attributes, spatial relations, and priors learned from data, conducting global optimization to generate a logical scene layout. Experimental results on a large-scale 3D indoor scene dataset substantiate that our method effectively learns inter-object relations and generates diverse and plausible indoor scenes. Comparative experiments and user studies further validate that our method surpasses the current state-of-the-art techniques in indoor scene generation and is comparable to real training scenes....
The existing cyber deception decision-making model based on game theory primarily focuses on the selection of spatial strategies, which ignores the optimal defense timing and can affect the execution of a defense strategy. Consequently, this paper presents a method for selecting deception strategies based on a multi-stage Flipit game. Firstly, based on the analysis of cyber deception attack and defense, we propose a concept of moving deception attack surface and analyze the characteristics of deception attack and defense interaction behaviors based on the Flipit game model. The Flipit game model is then utilized to create a single-stage deception spatial-temporal decision-making model. Additionally, we introduce the discount factor and transition probability based on a single-stage game model and construct a multi-stage cyber deception model. We provide the utility function of the multi-stage game model, and design a Proximal Policy Optimization algorithm based on deep reinforcement learning to compute the defender’s optimal spatial-temporal strategies. Finally, we utilize an application example to validate the effectiveness of the model and the advantages of the proposed algorithm in generating the multi-stage cyber deception strategy....
For achieving the carbon peaking and carbon neutrality goals, the renewable energy and the battery energy storage will be developed more rapidly. The participation of the solar-wind-battery renewable energy system (SWBS) in black-start can improve the resilience of the power grid. Aiming at the balance between the capacity demand of SWBS participating in black-start and the benefit of SWBS, this paper proposes a positive-sum game-based SWBS capacity configuration planning method. First, a levelized cost of energy- (LCOE-) based full life cycle index is presented and constructs an economic benefit model of SWBS considering the participation of wind and solar power plants in carbon trading. Second, a black-start capacity demand model is constructed considering the continuous power demand on the auxiliary devices of the operated thermal power generator. Taking the economic benefit of SWBS and the black-start capacity requirement into account, this paper establishes a positive-sum game-based SWBS capacity optimal configuration model, in which maximizing the economic benefits of SWBS as an objective. Third, the simulation analysis is carried out by using a real power system, and results verify that the proposed method can achieve the optimal balance between the feasibility of black-start and the economic benefit of the SWBS....
The rapid development of information technology affects numerous aspects of human life, including education. An example of IT application in education is game-based learning. Game-based learning has been implemented in various fields or subjects on various platforms. This is due to the potential of game-based learning to enhance the student engagement in the learning process. Nevertheless, the effectiveness of this method is still needs to be studied further. This systematic literature review aimed to explore about game mechanics that applied on current game-based learning researches, accompanied by the trend of technological utilization in research paper published in this domain. This study covered 30 journal and conference proceeding papers published from 2012-2022. The review was conducted using the Kitchenham method. Selected papers were then analyzed to determine the engagement model used in each paper (Feedback Model, Incentive and Achievement Model and Progression Model). Findings included the trend of research in this field (technology applied to each research, online feature, study majors/subject) are displayed based on the time paper were published. The result of the study indicated that all previous research used at least one of the engagement models, with 12 papers using all three models. In terms of technology, it was found that the adoption of web-based technology has been increasing in recent years, including online features which have also increased, along with the study subjects that implemented game-based learning. In summary, game-based learning can be applied in a wide range of subjects and platforms with the support of its feature, making learning more flexible....
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