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The intelligent monitoring of tool wear status and wear prediction are important factors affecting the intelligent development of the modern machinery industry. Many scholars have used deep learning methods to achieve certain results in tool wear prediction. However, due to the instability and variability of the signal data, some neural network models may have gradient decay between layers. Most methods mainly focus on feature selection of the input data but ignore the influence degree of different features to tool wear. In order to solve these problems, this paper proposes a dual-stage attention model for tool wear prediction. A CNN-BiGRU-attention network model is designed, which introduces the self-attention to extract deep features and embody more important features. The IndyLSTM is used to construct a stable network to solve the gradient decay problem between layers. Moreover, the attention mechanism is added to the network to obtain the important information of output sequence, which can improve the accuracy of the prediction. Experimental study is carried out for tool wear prediction in a dry milling operation to demonstrate the viability of this method. Through the experimental comparison and analysis with regression prediction evaluation indexes, it proves the proposed method can effectively characterize the degree of tool wear, reduce the prediction errors, and achieve good prediction results. [ABSTRACT FROM AUTHOR]

Prediction of building energy consumption using mathematical modeling is crucial for improving the efficiency of building energy utilization, assisting in building energy consumption planning and scheduling, and further achieving the goal of energy conservation and emission reduction. In consideration of the non-linear and non-smooth characteristics of building energy consumption time series data, a short-term, hybrid building energy consumption prediction model combining variational mode decomposition (VMD), a simulated annealing (SA) algorithm, and a deep belief network (DBN) is proposed in this study. In the proposed VMD-SA-DBN model, the VMD algorithm decomposes the time series into different modes to reduce the fluctuation of the data. The SA-DBN prediction model is built for each mode separately, and the DBN network structure parameters are optimized by the SA algorithm. The prediction results of each model are aggregated and reconstructed to obtain the final prediction output. The validity and prediction performance of the proposed model is evaluated on a publicly available dataset, and the results show that the proposed new model significantly improves the accuracy and stability of building energy consumption prediction compared with several typical machine learning methods. The mean absolute percent error (MAPE) of the VMD-SA-DBN model is 63.7%, 65.5%, 46.83%, 64.82%, 44.1%, 36.3%, and 28.3% lower than that of the long short-term memory (LSTM), gated recurrent unit (GRU), VMD-LSTM, VMD-GRU, DBN, SA-DBN, and VMD-DBN models, respectively. The results will help managers formulate more-favorable low-energy emission reduction plans and improve building energy efficiency. [ABSTRACT FROM AUTHOR]

Mobile Agent (MA) technology brings many benefits into Wireless Sensor Networks (WSNs), such as saving network bandwidth and enabling energy efficient mechanisms for collecting sensor data. Nowadays, itinerary planning for MAs is one of the most important features of the WSN. However, the way in which all dispatched MAs are routed inside the sensor networks must be intelligently planned to reduce energy consumption and improve information accuracy. There have been many research efforts designing itinerary planning algorithms to deploy multiple MAs in a given sensor network, where routes are generated so that MAs can follow different routes to collect data from sensor nodes efficiently and effectively. This paper proposes a new energy efficient Graph-based Static Mutli-Mobile Agent Itinerary Planning approach (GSMIP). GSMIP applies Directed Acyclic Graph (DAG) related techniques and divide sensor nodes into different groups based on the routes defined by MAs itineraries. MAs follow the predefined routes and only collect data from the groups they are responsible for. The experimental findings demonstrate the effectiveness and superiority of the proposed approach compared to the existing approaches in terms of energy consumption and task delay (time). [ABSTRACT FROM AUTHOR]