To implement the quality-relevant monitoring scheme for batch processes with multiple output modes, this paper presents a novel methodology based on stochastic programming. Bringing together tools from stochastic programming and ensemble learning, the developed methodology focuses on the robust monitoring of process quality-relevant variables by taking the stochastic nature of batch process parameters explicitly into consideration. To handle the problem of missing data and lack of historical batch data, a bagging approach is introduced to generate individual quality-relevant sub-datasets, which are used to construct the corresponding monitoring sub-models. For each model, stochastic programming is used to construct an optimal quality trajectory, which is regarded as the reference for online quality monitoring. Then, for each sub-model, a corresponding control limit is obtained by computing historical residuals between the actual output and the optimal trajectory. For online monitoring, the current sample is examined by all sub-models, and whether the monitoring statistic exceeds the control limits is recorded for further analysis. The final step is ensemble learning via Bayesian fusion strategy, which is under the probabilistic framework. The implementation and effectiveness of the developed methodology are demonstrated through two case studies, including a numerical example, and a simulated fed-batch penicillin fermentation process.

The Shack–Hartmann wavefront sensor (SHWFS) traditionally employs the center of gravity (CoG), windowing, and thresholding CoG (TCoG) algorithms to measure wavefront aberrations. Nevertheless, these algorithms yield low accuracy in a low signal-to-noise ratio (SNR) environment, and therefore, we propose a measurement algorithm based on the Kalman filter to correct the three algorithms’ centroid position of the Shack–Hartmann spot. When the Euclidean distance between the reference centroid in a sub-aperture and the measurement centroid exceeds a certain threshold, the sub-apertures centroid position is untrusted and must be corrected. Several simulation experiments are conducted in three environments: no noise, Poisson noise, and Gaussian noise; they demonstrate that the proposed algorithm effectively improves the centroid’s accuracy by more than 10% and ensures real-time performance for the adaptive optics system. In addition, experimental results on wavefront reconstruction tasks reveal that our algorithm produces wavefronts close to the original.

Wireless sensor networks (WSN) are becoming increasingly promising in practice. As the predeployment design and optimization are usually unpractical in random deployment scenarios, the global optimum of the WSN’s performance is achievable only if the topology dependent self-organizing process acquires the overview of the WSN, in which the boundary is the most important. The idea of this paper comes from the fact that contours only break on the geometrical boundary and the WSN are discrete sampling systems of real environments. By simulating a diffusion process in discrete form, the end point of semi-contours suggests the boundary nodes of a WSN. The simulation cases show the algorithm is well worked in WSN with average degree higher than 10. The boundary recognition could be very valuable for other algorithms dedicated to optimize the overall performance of WSN.