Aiming at the problem of low prediction accuracy of traditional prediction models due to the limited labeled sample data and the imbalance of multitimescale sample data in the cement production process, a cement clinker-free calcium oxide (f-CaO) data enhancement and prediction model based on semisupervised prediction Wasserstein generative adversarial networks (SSP-WGANs) is proposed in this article. The model is constructed by WGAN and a prediction model. In the generator of WGAN, the traditional noise input is replaced by time-series matrices composed of related variables affecting the f-CaO content of cement clinker. The generator maps the input high-dimensional time-series data into low-dimensional labeled values through its internal convolutional layer, which can fill in the missing labeled values of the input unlabeled samples. The generated labels are spliced with the related variables affecting the f-CaO content of clinker according to the timescale relationship and are uniformly sent to the discriminator with the real sample pairs for discrimination, thus eliminating the influence of multiple timescales. The generated labeled data are matched with the unlabeled data and used to expand the training set of the prediction model composed of CNN-GRU, which significantly improves the prediction accuracy of the model. The results show that the SSP-WGAN model with data enhancement has higher accuracy and stronger robustness.
According to the too high density of nodes in Wireless Sensor Networks(WSN) and imbalanced energy consumption of nodes,a distributed coverage control algorithm based on energy(DCCABE) is proposed.This algorithm is based on probabilistic coverage model and computes the area coverage probability of nodes to judge the redundancy according to the sequences which are arranged by the remain energy of nodes and changes the redundant nodes into sleeping state.Simulation results show that DCCABE can effectively reduce the redundancy of nodes and prolong the network lifetime.
The cement rotary kiln firing process is complex, and raw material fluctuations and kiln condition changes can cause changes in the actual model characteristics of the production. Aiming at the problem that the model prediction parameters of the model are difficult to select and the prediction accuracy is low, a differential evolution based DE-TVD-DBN structure optimization model. The DE-TVD-DBN forward reconstruction error according to the DE-TVD-DBN forward training model can reflect the characteristics of the restricted Boltzmann machine(RBM) to minimize the DE-TVD-DBN forward training. The forward reconstruction error is the objective function, and the DE-TVD-DBN structure optimization model is constructed. Considering the complexity and precision of the optimization process, the differential evolution algorithm is used to solve the model iteratively. Experiments were carried out using the actual data. The results show that the model structure selected by the model has higher precision in predicting the electricity consumption of the cement rotary kiln, and effectively reduces the complexity of the optimization process. The automatic optimization of the model structure is realized.
One of the key indicators for evaluating finished cement products is the cement specific surface area. The soft sensor model for cement specific surface area serves as the foundation for scheduling cement production and is critical for increasing cement quality. To solve the issue of soft sensor models of cement specific surface area caused by the non-linearity, time lag and strong coupling of the cement industry’s big data, the soft sensor model of Time Convolution Network (TCN) and Attention Simple Recurrent Unit (ASRU) Network is proposed.TCN suppresses the issue of feature redundancy due to data coupling. Meanwhile, an improved network structure ASRU is established considering the long time series of process industrial data. ASRU can quickly screen out the higher value information from a vast amount of information to enhance the sensitivity to information. TCN-ASRU can capture the spatiotemporal features in the input data and the dynamic response relationship on the time sequence to solve the problems including time-varying time delay. The model was trained and validated on a cement specific surface area data-set collected. The results demonstrated that the model has the higher prediction accuracy and good generalization ability.
Advanced packaging offers a new design paradigm in the post-Moore era, where many small chiplets can be assembled into a large system. Based on heterogeneous integration, a chiplet-based accelerator can be highly specialized for a specific workload, demonstrating extreme efficiency and cost reduction. To fully leverage this potential, it is critical to explore both the architectural design space for individual chiplets and different integration options to assemble these chiplets, which have yet to be fully exploited by existing proposals. This paper proposes Monad, a cost-aware specialization approach for chiplet-based spatial accelerators that explores the tradeoffs between PPA and fabrication costs. To evaluate a specialized system, we introduce a modeling framework considering the non-uniformity in dataflow, pipelining, and communications when executing multiple tensor workloads on different chiplets. We propose to combine the architecture and integration design space by uniformly encoding the design aspects for both spaces and exploring them with a systematic ML-based approach. The experiments demonstrate that Monad can achieve an average of 16% and 30% EDP reduction compared with the state-of-the-art chiplet-based accelerators, Simba and NN-Baton, respectively.
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