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Sodium hypochlorite bleaching washing process has been broadly carried out in denim garment industrial production. However, the quantitative relationships between process variables and bleaching performances have not been illustrated explicitly. Hence, it is impractical to determine values of the variables that can achieve the optimal production cost while satisfying the requirements of customers. This paper proposes an optimization methodology by combining ensemble of surrogates (ESs) with particle swarm optimization (PSO) to optimize production cost of chlorine bleaching for denim. The methodology starts from the data collections by conducting a Taguchi L25 (56) orthogonal experiment with the process variables and metrics for evaluating bleaching performances. Based on the data, the quantitative relationships are separately constructed by using RBFNN, SVR, RF and ensemble of them. Then, accuracies of the surrogates are evaluated and it proves that the ESs outperforms the others. Later, the production cost optimization model is proposed and PSO is utilized to solve it, while a case study is given to depict the optimization process and verify the effectiveness of the proposed hybrid ESs-PSO approach. Overall, the ESs-PSO approach shows great capability of optimizing production cost of sodium hypochlorite bleaching washing for denim.

When the desired signal is present in the training snapshots, the performance of conventional orthogonal projection (OP) adaptive beamforming degrades severely due to the desired signal cancellation effect. To overcome this deficiency, the improved orthogonal projection (IOP) adaptive beamforming by using reconstructed interference covariance matrix is proposed. In the proposed algorithm, the interference covariance matrix is firstly reconstructed by integrating the Capon spatial spectrum over a region separated from the desired signal direction. Subsequently, the jammer subspace is estimated, and then the adaptive weight vector is calculated using conventional OP algorithm. The simulation results show that the corresponding output signal-to-jammer-plus-noise ratio (SJNR) performance of proposed IOP algorithm is almost same with the optimum beamformer with the desired signal in the training snapshots. Therefore, the proposed IOP algorithm is significantly effective for the actual system.