Road Recognition Based on Vehicle Vibration Signal and Comfortable Speed Strategy Formulation Using ISA Algorithm.

When a vehicle is being driven, it is excited by the road roughness and generates its own vibration. In order to improve the vehicle's riding comfort and the physical-mental health of passengers in the vehicle, this paper proposes a formulation method for a comfortable speed strategy and the technical route of its application. According to international standard ISO 2631-1, the relationship between the weighted root-mean-square acceleration value and comfortable vehicle speed is analyzed. The simulation test platform of the road roughness signal and vehicle vibration signal is built by using the filtering white noise method and the second Lagrange equation through Matlab/Simulink. Combined with the simulation platform, this paper extracts seven characteristics with statistical properties from the time-domain signal and obtains 500 sample data. Random forest (RF), extreme learning machine (ELM), and radial basis function neural network (RBF-NN) are applied to identify roads. Two comfortable speed strategy formulation methods based on the improved simulated annealing (ISA) algorithm are proposed and compared according to the solution effect of each grade of comfortable speed. The results show that the simulated signals of each grade road roughness are accurate. Road recognition can be effectively carried out using the statistical characteristics of vehicle vibration acceleration signals. ELM has high recognition accuracy and fast execution speed. The ISA-II algorithm has a low solution error of comfortable speed and a low computation time. The comfortable speed of the research vehicle on different road grades showed a great difference.

Wide-Gap Brazing of K417G Alloy Assisted by In Situ Precipitation of M3B2 Boride Particles.

In this study, K417G Ni-based superalloy with a 20-mm gap was successfully bonded at 1200 °C using powder metallurgy with a powder mixture. The results indicated that the microstructure and mechanical properties of the as-bonded alloy were highly dependent on the brazing time (15-45 min), mainly due to the precipitation and distribution characteristics of M3B2 boride particles. Specifically, alloy brazed for 30 min exhibited desirable mechanical properties, such as a high tensile ultimate strength of 971 MPa and an elongation at fracture of 6.5% at room temperature, exceeding the balance value (935 MPa) of the base metal. The excellent strength and plasticity were mainly due to coherent strengthening and dispersion strengthening of the in situ spherical and equiaxed M3B2 boride particles in the γ + γ' matrix. In addition, the disappearance of dendrites and the homogenization of the microstructure are other factors that cannot be excluded. This powder metallurgy technique, which can avoid the eutectic transformation of traditional brazing, provides a new effective method for wide-gap repair of alloy materials.

Study on Microstructure and Mechanical Properties of WC-10Ni3Al Cemented Carbide Prepared by Different Ball-Milling Suspension.

In this paper, WC-10Ni3Al cemented carbides were prepared by the powder metallurgy method, and the effects of ball-milling powders with two different organic solvents on the microstructure and mechanical properties of cemented carbides were studied. We show that the oxygen in the organic solvent can be absorbed into the mixed powders by ball-milling when ethanol (CH3CH2OH) is used as a ball-milling suspension. This oxygen leads to the formation of α-Al2O3 during sintering, which improves the fracture toughness, due to crack deflection and bridging, while the formation of η-phase (Ni3W3C) inhibits the grain growth and increases the hardness. Alternatively, samples milled using cyclohexane (C6H12) showed grain growth during processing, which led to a decrease in hardness. Therefore, the increase of oxygen content from using organic solvents during milling improves the properties of WC-Ni3Al composites. The growth of WC grains can be inhibited and the hardness can be improved without loss of toughness by self-generating α-Al2O3 and η-phase (Ni3W3C).

A hybrid gene selection method based on gene scoring strategy and improved particle swarm optimization.

BACKGROUND: Gene selection is one of the critical steps in the course of the classification of microarray data. Since particle swarm optimization has no complicated evolutionary operators and fewer parameters need to be adjusted, it has been used increasingly as an effective technique for gene selection. Since particle swarm optimization is apt to converge to local minima which lead to premature convergence, some particle swarm optimization based gene selection methods may select non-optimal genes with high probability. To select predictive genes with low redundancy as well as not filtering out key genes is still a challenge. RESULTS: To obtain predictive genes with lower redundancy as well as overcome the deficiencies of traditional particle swarm optimization based gene selection methods, a hybrid gene selection method based on gene scoring strategy and improved particle swarm optimization is proposed in this paper. To select the genes highly related to out samples' classes, a gene scoring strategy based on randomization and extreme learning machine is proposed to filter much irrelevant genes. With the third-level gene pool established by multiple filter strategy, an improved particle swarm optimization is proposed to perform gene selection. In the improved particle swarm optimization, to decrease the likelihood of the premature of the swarm the Metropolis criterion of simulated annealing algorithm is introduced to update the particles, and the half of the swarm are reinitialized when the swarm is trapped into local minima. CONCLUSIONS: Combining the gene scoring strategy with the improved particle swarm optimization, the new method could select functional gene subsets which are significantly sensitive to the samples' classes. With the few discriminative genes selected by the proposed method, extreme learning machine and support vector machine classifiers achieve much high prediction accuracy on several public microarray data, which in turn verifies the efficiency and effectiveness of the proposed gene selection method.