The prediction of key quality variables plays an important role in industrial status identification and monitoring. Due to process disturbance and hard device limitation, data collection in modern industries often exhibits high noise and irregular data sampling. To solve the above problems, this article proposes a stacked supervised and reconstructed input denoising autoencoder integrated with internal attention long short-term memory (SSRDAE-IALSTM) network for soft sensing modeling. First, a stacked supervised and reconstructed input denoising autoencoder (SSRDAE) is designed. Compared with the original DAE, each supervised and reconstructed input DAE (SRDAE) can simultaneously reconstruct the process data and quality data at the output layer, aiming to reduce information loss and extract quality-related features. Second, the denoised features are fed into the interval attention LSTM (IALSTM) to adjust the influence of different historical samples on the current sample in irregular sampling data to capture long-term temporal features. Finally, performance validations are carried out on an industrial debutanizer column and a penicillin fermentation process. The experimental results show that the proposed model can enhance the learning ability of process features and obtain better prediction performance than other comparison methods.

In this paper we present a new reconstruction algorithm. This algorithm is based on the Advanced Marking Scheme (AMS), and it works with a two-dimensional threshold mfd decides if a node is on the attack path by judging the situation of the edge of the packet and the Hash value match, then consequently reduces the time of reconstruction and overhead and improves the accuracy. Simulation results from the experiments carried on in the network test-bed show that obtained results are the same as theoretical results. Performance analysis and simulation experiment results shows, compared with the conventional methods, two-dimensional threshold reconstruction algorithm improves the performance of IP traceback technique obviously.