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The FeF3-coated Li[Li0.2Mn0.54Ni0.13Co0.13]O2 cathode materials were synthesized via a wet chemical process followed by a solid state reaction. The physical properties of the as-prepared samples were conducted using X-ray diffraction, scanning electron microscopy, and transmission electron microscopy. The results indicated that the surface of cathode particles was covered by FeF3 film (about 5–15 nm thick). Compared with the bare cathode, the FeF3-coated Li[Li0.2Mn0.54Ni0.13Co0.13]O2 one delivered the higher coulombic efficiency, better rate capability, longer cycle life, and better structure stability. A high capacity retention of 95% (190 mAh g− 1) after 100 cycles at 0.5 C rate was obtained for the FeF3-coated Li[Li0.2Mn0.54Ni0.13Co0.13]O2 cathode. Besides, the results from cyclic voltammetry and electrochemical impedance spectra revealed that the noteworthy enhanced electrochemical properties of the surface-modified sample were due to the presence of FeF3 coating layer, which suppressed the side reaction between the Li[Li0.2Mn0.54Ni0.13Co0.13]O2 particles with electrolyte and further stabilized the Li[Li0.2Mn0.54Ni0.13Co0.13]O2 structure with cycling.

Beating is the most significant process in papermaking technique that can modify the fiber morphology, sheet-formation ability of fiber suspension and properties of the wet-laid mat. In this paper, flame attenuated glass wool was beaten in water at various revolutions, ranging from 1500r to 12000r. Compared to suspensions beaten at 0r and 1500r where glass fibers simply aggregated in water, glass wool suspensions beaten at 3000r to 8000r were homogeneous with separated and randomly arranged glass fibers. Further, suspensions beaten at revolutions above 8000r were uniform but with a few broken glass fibers. The dewatering characteristics of fiber suspensions were analyzed based on fiber plugging model. Drainage resistances of flame attenuated glass wool suspension increased dramatically from 22 °SR (unbeaten) to 29 °SR (at 1500r) and then maintained at this value till 12000r. Density and porosity of the fabricated wet-laid mat was 95 kg/m 3 and 94%, respectively. Maximum tensile strength of 170 N/m for the wet-laid mat was achieved at 8000r, which then decreased significantly to 100 N/m when the revolutions exceed 10000r.

Prefabricated construction has attracted research interest as it can significantly improve the energy, cost, and time efficiency of construction. However, dispatching the required prefabricated components to construction sites in a prefabricated system is challenging. To address this issue, we modeled the dispatching problem as a special type of vehicle routing problem with time windows (VRPTW) and solved it by using an improved artificial bee colony (IABC) algorithm. First, to efficiently solve the cross-synchronization problem that occurs in prefabricated systems, two problem-specific lemmas were derived. Then, a hybrid initialization strategy was developed to generate feasible and efficient solutions and a well-designed encoding repair strategy was utilized to make solutions feasible. Finally, a variable length local search strategy was embedded to enhance the exploitation ability. To verify the performance of the proposed IABC algorithm, 55 instances were generated and used for simulation tests. Three efficient algorithms, including the two-phase genetic algorithm (TPGA), improved tabu search algorithm (ITSA), and adaptive large neighborhood search (ALNS) heuristic, were selected for detailed comparisons. Our simulation results show that, considering the energy consumption metric, the proposed algorithm yields average deviations of about 0.099, 0.096, and 0.143 times the values obtained with the TPGA, ITSA, and ALNS heuristic, respectively. The simulation results confirmed that the proposed algorithm can solve the VRPTW in prefabricated systems with high efficiency.

The linguistic dynamic systems (LDSs) based on type-1 fuzzy sets can provide a powerful tool for modeling, analysis, evaluation and control of complex systems. However, as pointed out in earlier studies, it is much more reasonable to take type-2 fuzzy sets to model the existing uncertainties of linguistic words. In this paper, the LDS based on type-2 fuzzy sets is studied, and its reasoning process is realized through the perceptual reasoning method. The properties of the perceptual reasoning method based LDS (PR-LDS) are explored. These properties demonstrated that the output of PR-LDS is intuitive and the computation complexity can be reduced when the consequent type-2 fuzzy numbers in the rule base satisfy some conditions. Further, a data driven method for the design of the PR-LDS is provided. At last, the effectiveness and rationality of the proposed data-driven method are verified by an example.

The level adjustment of cable-driven parallel mechanism is challenging due to the difficulty in obtaining an accurate mathematical model and the fact that different sources of uncertainties exist in the adjustment process. This paper presents a type-2 fuzzy neural network (T2FNN) based inverse control scheme for the level adjustment process. The T2FNN, whose consequent interval weights are trained by the iterative least squares estimation method, is used to approximate the inverse dynamics of the process and to handle uncertainties. Finally, the proposed control scheme and its counterpart — type-1 fuzzy neural network based inverse control scheme — are compared and implemented for leveling the cable-driven parallel mechanism.