A simulation testbed for the engine brake when the vehicle coasts on lasting downhill was built, and the engine brake torque curve was obtained by the test on the testbed. A twomass inertial model was established for the transmission system for the condition of vehicle lasting downhill. The simulation was performed using MATLAB/SIMULINK to study the CVT control strategies when the engine brakes on long downhill of the vehicle and the encouraging results were achieved.

Aiming at minimizing the total cost of the bus operation system as the optimization objective, considering the route choice behaviors of the travelers, a bilevel programming model for optimizing the bus frequencies was presented. In the model, the upperlevel optimizes the bus frequencies from the result of passenger assignment, and the lowerlevel assigns the bus travelers based on the optimal frequencies. An iterative algorithm integrating a labelmarking method and a new heuristic algorithmshuffled complex evolution method(SCEUA) was designed to solve the lower and upper models. Taking Dalian city bus system as an example, the model and algorithms were tested against the system data, and the results show that the presented model and algorithms are reasonable and effective.

The traditional MUSIC algorithm requires the covariance matrix and its eigendecomposition or singular value decomposition that result in the increase of the computational complexity. To overcome such problem, a method for source parameter estimation in the environments of farfield and nearfield was proposed based on multistage Wiener filters. It can replace the computation and decomposition of the covariance matrix under certain conditions. It has less computational complexity, which is particularly significant in the situation of more numbers of arrays and snapshots. The proposed method is validated by case studies.

A new adaptive recursive algorithm for eigenvector estimation based on the relations between KLT and sliding DCT was presented. On the basis of the equivalence of weight vector of an adaptive filter and the eigenvector, choosing a reasonable filter expectation response, the eigenvector was estimated. Compared with current eigenvector calculation methods such as SVD, the proposed algorithm can effectively reduce both amount of calculation and complexity, be easily implemented on time, and possesses high estimation accuracy. Applying this algorithm in spectrum estimation with computer simulations, it is known that the proposed recursive eigenvector estimation algorithm attains higher spectrum estimation solution comparing with standard MUSIC spectrum estimation method with less calculation, which proves the effectiveness and advantage of the algorithm.