To solve the problems such as low accuracy and low retrieval performance in feature extraction of environmental sound data from Internet consumer finance scenario, a novel method for environmental sound filtering and feature extraction (VF-EFENet)
Aiming at the problems of secondary pollution and resource waste caused by inaccurate input of coal and ammonia in coal-fired power plant, an optimal controlling method of combustion and denitration coordinated operation based on Deep Deterministic Policy Gradient (DDPG) is proposed in this paper.First, the environmental model is constructed by the Stacking algorithm to predict the NO x emission concentration of the combustion and denitration system, which provides environmental state feedback for the optimal controlling model. Second, the optimization controlling model is constructed based on the DDPG algorithm within the standard limitation of denitration efficiency and NO x emission concentration. This model takes the minimization of comprehensive cost as its optimization objective to realize the optimal control of controllable variables in the cooperative operation process of combustion and denitration. The experimental results of real operational data from 1000 MW boiler unit in a power plant locating in south China show that the optimization results of coordinated operation for the combustion and denitration system are better than single-stage optimization results. In addition, the total cost is reduced by 1%-3% on average compared with before optimization.
The knowledge-enhanced BERT model solves the problem of lacking knowledge in downstream tasks by injecting external expertize, and achieves higher accuracy compared with BERT model. However, owning to large-scale external knowledge is utilized into knowledge-enhanced BERT, some shortcomings comes such as information noise, lower accuracy and weak generalization ability, and so on. To solve this problem, a knowledge enhancement method BERTBooster which combines incremental learning and gradient optimization is proposed. BERTBooster disassembles the input text corpus into entity noun sets through entity noun recognition, and uses the incremental learning task denoising entity auto-encoder to create an incremental task set of entity nouns and external knowledge triples. Furthermore, BERTBooster introduces a new gradient optimization algorithm ChildTuningF into BERT model to improve the generalization ability. BERTBooster can effectively improve the factual knowledge cognition ability of CAGBERT model and improve the accuracy of the model in downstream tasks. Experiments are carried out on six public data sets such as Book_Review, LCQMC, XNLI, Law_QA, Insureace_QA, and NLPCC-DBQA. The experimental results show that the accuracy
Terahertz (THz) wave is an electromagnetic wave with a frequency between far infrared ray and millimeter wave, which is widely used in hazardous material detection for its waveband fingerprint spectroscopy. THz time-domain spectroscopy technology based on deep learning can be used for nondestructive detection of various hazardous materials by recognizing the fingerprint spectrum of substances. However, due to the high cost of collecting spectral data, training samples are not easy to obtain and scarce for classification models, which leads to poor training effectiveness and low accuracy of classification. To address this problem, a fully connected layer-based auxiliary classifier generative adversarial network (FC-ACGAN) data augmentation method is proposed in this paper, we realized the generator and discriminator with fully connected layers to fit original data distribution better and generate data with higher quality. First, THz time-domain spectral data from seven flammable liquids were augmented using Mixup and FC-ACGAN, and then we fed the generated data set and expanded data set into Residual Network (ResNet), convolutional neural network, fully convolutional network, and multilayer perceptron for training.
Aiming at the problems of complex structure, high components coupling, and difficultly monitoring of the whole health status with the industrial robot, a metric learning-based whole health indicator model is proposed.First, according to the more obvious degradation characteristics of industrial robots during accelerated operation, the accelerated signal is segmented and then the time-domain features are extracted. Second, the longterm and short-term memory (LSTM) network combined with the multihead attention is used to construct the network model, and the metric learning method is adopted to learn the similarity measurement method of the industrial robot monitoring data. Finally, the similarity measure method got from metric learning is used to construct the whole health indicator, which describes the whole degradation trend of the industrial robot.The experiments are based on the real accelerated aging data set from industrial robots. The results show that the proposed model can effectively construct the whole health indicator for industrial robots. The average trend of the proposed model reaches 0.9769. The average monotonicity reaches 0.5666, which is 0.1748, 0.1577, and 0.1492 higher than the similarity measurement method based on Euclidean distance, Markov distance, and LSTM.
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