Tongji University

About: Tongji University is a education organization based out in Shanghai, China. It is known for research contribution in the topics: Computer science & Population. The organization has 76116 authors who have published 81176 publications receiving 1248911 citations. The organization is also known as: Tongji & Tóngjì Dàxué.

Sandwich-Like Fe&TiO 2 @C Nanocomposites Derived from MXene/Fe-MOFs Hybrids for Electromagnetic Absorption

Abstract: Electromagnetic pollution has been causing a series of problems in people’s life, and electromagnetic absorbers with lightweight and broad absorbing bandwidth properties are widely desired. In this work, novel sandwich-like 2D laminated Fe&TiO2 nanoparticles@C nanocomposites were rationally designed and successfully developed from the MXene–MOFs hybrids. The formation of Fe and rutile-TiO2 nanoparticles sandwiched by the two-dimensional carbon nanosheets provided strong electromagnetic energy attenuation and good impedance matching for electromagnetic wave (EMW) absorption. As expected, the nanocomposites achieved a broad effective absorption bandwidth of 6.5 GHz at a thickness of only 1.6 mm and the minimum reflection loss (RL) value of − 51.8 dB at 6.6 GHz with a thickness of 3 mm. This work not only provides a good design and fabricating concept for the laminated metal and functional nanoparticles@C nanocomposites with good EMW absorption, but also offers an important guideline to fabricate various two-dimensional nanocomposites derived from the MXene precursors.

Group Role Assignment via a Kuhn–Munkres Algorithm-Based Solution

Abstract: Role assignment is a critical task in role-based collaboration. It has three steps, i.e., agent evaluation, group role assignment, and role transfer, where group role assignment is a time-consuming process. This paper clarifies the group role assignment problem (GRAP), describes a general assignment problem (GAP), converts a GRAP to a GAP, proposes an efficient algorithm based on the Kuhn-Munkres (K-M) algorithm, conducts numerical experiments, and analyzes the solutions' performances. The results show that the proposed algorithm significantly improves the algorithm based on exhaustive search. The major contributions of this paper include formally defining the GRAPs, giving a general efficient solution for them, and expanding the application scope of the K-M algorithm. This paper offers an efficient enough solution based on the K-M algorithm that outperforms significantly the exhaustive search approach.

Centralized Charging Strategy and Scheduling Algorithm for Electric Vehicles Under a Battery Swapping Scenario

Abstract: Centralized charging of electric vehicles (EVs) based on battery swapping is a promising strategy for their large-scale utilization in power systems. The most outstanding feature of this strategy is that EV batteries can be replaced within a short time and can be charged during off-peak periods or on low electric price and scheduled in any battery swap station. This paper proposes a novel centralized charging strategy of EVs under the battery swapping scenario by considering optimal charging priority and charging location (station or bus node in a power system) based on spot electric price. In this strategy, a population-based heuristic approach is designed to minimize total charging cost, as well as to reduce power loss and voltage deviation of power networks. We introduce a dynamic crossover and adaptive mutation strategy into a hybrid algorithm of particle swarm optimization and genetic algorithm. The resulting algorithm and several others are executed on an IEEE 30-bus test system, and the results suggest that the proposed one is effective and promising for optimal EV centralized charging.