LSTM-based Flow Prediction

In this paper, a method of prediction on continuous time series variables from the production or flow -- an LSTM algorithm based on multivariate tuning -- is proposed. The algorithm improves the traditional LSTM algorithm and converts the time series data into supervised learning sequences regarding industrial data's features. The main innovation of this paper consists in introducing the concepts of periodic measurement and time window in the industrial prediction problem, especially considering industrial data with time series characteristics. Experiments using real-world datasets show that the prediction accuracy is improved, 54.05% higher than that of traditional LSTM algorithm.Comment: 8 pages, 11 figure

An offline fault diagnosis method for planetary gearbox based on empirical mode decomposition and adaptive multi-scale morphological gradient filter

Planetary gearbox is increasingly used in many kinds of rotary machinery in recent years. Due to the specialty of its structure, fault diagnosis for planetary gearbox is very difficult compared with the fixed shaft gearbox. This paper proposed an offline fault diagnosis method for planetary gearbox based on empirical mode decomposition and adaptive multi-scale morphological gradient filter. Firstly, the framework of the method proposed in this paper was introduced. Then, experimental data and industrial data were utilized to validate the effectiveness of the method. And the combination of empirical mode decomposition and adaptive multi-scale morphological dilation-erosion gradient filter was found very suitable to be used in the planetary gearbox fault diagnosis compared with other five filters. The proposed method was demonstrated to be of good performance on both extracting faults characteristic frequency and de-noising

Representation Learning with Ordered Relation Paths for Knowledge Graph Completion

Incompleteness is a common problem for existing knowledge graphs (KGs), and the completion of KG which aims to predict links between entities is challenging. Most existing KG completion methods only consider the direct relation between nodes and ignore the relation paths which contain useful information for link prediction. Recently, a few methods take relation paths into consideration but pay less attention to the order of relations in paths which is important for reasoning. In addition, these path-based models always ignore nonlinear contributions of path features for link prediction. To solve these problems, we propose a novel KG completion method named OPTransE. Instead of embedding both entities of a relation into the same latent space as in previous methods, we project the head entity and the tail entity of each relation into different spaces to guarantee the order of relations in the path. Meanwhile, we adopt a pooling strategy to extract nonlinear and complex features of different paths to further improve the performance of link prediction. Experimental results on two benchmark datasets show that the proposed model OPTransE performs better than state-of-the-art methods

A Deep Belief Network Based Model for Urban Haze Prediction

In order to improve the accuracy of urban haze prediction, a novel deep belief network (DBN)-based model was proposed. Firstly, data pertaining to both air quality and the environment (e.g. meteorology) data was monitored and collected. The primary haze influencing elements were discovered by analyzing the correlations between each of the meteorological factors and haze. Secondly, a DBN combined with multilayer restricted Boltzmann machines and a single-layer back propagation network was applied. Thirdly, the meteorological data predictions were carried out by using a competitive adaptive-reweighed method. A stable model was established by big-data training and its accuracy was verified by experiments. Results demonstrate that the pollution haze occurs in accordance with regular laws, and is greatly affected by wind direction, atmospheric pressure, and seasons. The correlation coefficient (CC) between the actual haze value and the prediction of the proposed model is 0.8, and the mean absolute error (MAE) is 26 μg/m3. Compared with the traditional prediction algorithms, the CC is improved by 18 % on average, while the MAE is reduced by 15.7 μg/m3. The proposed method has a good prospect to predict haze and investigate the main causes of it. This study provides data support for urban haze prevention and governance

Dummy Molecularly Imprinted Polymers-Capped CdTe Quantum Dots for the Fluorescent Sensing of 2,4,6-Trinitrotoluene

Molecularly imprinted polymers (MIPs) with trinitrophenol (TNP) as a dummy template molecule capped with CdTe quantum dots (QDs) were prepared using 3-aminopropyltriethoxy silane (APTES) as the functional monomer and tetraethoxysilane (TEOS) as the cross linker through a seedgrowth method via a sol gel process (i.e., DMIP@QDs) for the sensing of 2,4,6-trinitrotoluene (TNT) on the basis of electron-transfer-induced fluorescence quenching. With the presence and increase of TNT in sample solutions, a Meisenheimer complex was formed between TNT and the primary amino groups on the surface of the QDs. The energy of the QDs was transferred to the complex, resulting in the quenching of the QDs and thus decreasing the fluorescence intensity, which allowed the TNT to be sensed optically. DMIP@QDs generated a significantly reduced fluorescent intensity within less than 10 min upon binding TNT. The fluorescence-quenching fractions of the sensor presented a satisfactory linearity with TNT concentrations in the range of 0.8-30 mu M, and its limit of detection could reach 0.28 mu M. The sensor exhibited distinguished selectivity and a high binding affinity to TNT over its possibly competing molecules of 2,4-dinitrophenol (DNP), 4-nitrophenol (4-NP), phenol, and dinitrotoluene (DNT) because there are more nitro groups in TNT and therefore a stronger electron-withdrawing ability and because it has a high similarity in shape and volume to TNP. The sensor was successfully applied to determine the amount of TNT in soil samples, and the average recoveries of TNT at three spiking levels ranged from 90.3 to 97.8% with relative standard deviations below 5.12%. The results provided an effective way to develop sensors for the rapid recognition and determination of hazardous materials from complex matrices.Molecularly imprinted polymers (MIPs) with trinitrophenol (TNP) as a dummy template molecule capped with CdTe quantum dots (QDs) were prepared using 3-aminopropyltriethoxy silane (APTES) as the functional monomer and tetraethoxysilane (TEOS) as the cross linker through a seedgrowth method via a sol gel process (i.e., DMIP@QDs) for the sensing of 2,4,6-trinitrotoluene (TNT) on the basis of electron-transfer-induced fluorescence quenching. With the presence and increase of TNT in sample solutions, a Meisenheimer complex was formed between TNT and the primary amino groups on the surface of the QDs. The energy of the QDs was transferred to the complex, resulting in the quenching of the QDs and thus decreasing the fluorescence intensity, which allowed the TNT to be sensed optically. DMIP@QDs generated a significantly reduced fluorescent intensity within less than 10 min upon binding TNT. The fluorescence-quenching fractions of the sensor presented a satisfactory linearity with TNT concentrations in the range of 0.8-30 mu M, and its limit of detection could reach 0.28 mu M. The sensor exhibited distinguished selectivity and a high binding affinity to TNT over its possibly competing molecules of 2,4-dinitrophenol (DNP), 4-nitrophenol (4-NP), phenol, and dinitrotoluene (DNT) because there are more nitro groups in TNT and therefore a stronger electron-withdrawing ability and because it has a high similarity in shape and volume to TNP. The sensor was successfully applied to determine the amount of TNT in soil samples, and the average recoveries of TNT at three spiking levels ranged from 90.3 to 97.8% with relative standard deviations below 5.12%. The results provided an effective way to develop sensors for the rapid recognition and determination of hazardous materials from complex matrices