Training High Quality Spam-detection Models Using Weak Labels

To be effective in detecting spam in online content sharing networks, it is necessary that techniques used to detect spam have good precision, high recall, and the ability to adapt to new types of spam. A bottleneck in developing such machine learning techniques is the lack of availability of high quality labeled training data. Human labeling to obtain high quality labeled data is expensive and not scalable. Current approaches such as unsupervised learning or semi-supervised learning can only produce low quality labels. Generally, the present disclosure is directed to a weak supervision approach to train a machine learning model to detect spam content items. Weak labels are generated for content items in training data using various techniques such as rules that encode domain knowledge and/or anomaly detection techniques such as unsupervised machine learning/ clustering or semi-supervised machine learning. The accuracy of the various techniques is estimated based on observed agreements/ disagreements in the weak labels. The weak labels are combined into a single value (e.g., per content item) that is used as a probabilistic training label to train a machine learning model using supervised learning that is noise aware. In the training, a penalty is applied for deviation from the probabilistic label such that the penalty is higher for a label associated with a higher confidence and lower for a label associated with a lower confidence. The model thus trained can be used to detect spam content

Rolling bearing fault identification using multilayer deep learning convolutional neural network

The vibration signal of rolling bearing is usually complex and the useful fault information is hidden in the background noise, therefore, it is a challenge to identify rolling bearing faults from the complex vibration environment. In this paper, a novel multilayer deep learning convolutional neural network (CNN) method to identify rolling bearing fault is proposed. Firstly, in order to avoid the influence of different characteristics of the input data on the identification accuracy, a normalization preprocessing method is applied to preprocess the vibration signals of rolling bearings. Secondly, a multilayer CNN based on deep learning is designed in this paper to improve the fault identification accuracy of rolling bearing. Simulation data and experimental data analysis results show that the proposed method has better performance than SVM method and ANN method without any manual feature extractor design

Data augmentation and intelligent fault diagnosis of planetary gearbox using ILoFGAN under extremely limited samples

Though the existing generative adversarial networks (GAN) have the potential for data augmentation and intelligent fault diagnosis of planetary gearbox, it remains difficult to deal with extremely limited training samples and effectively fuse the representative and diverse information. To tackle the above challenges, an improved local fusion generative adversarial network (ILoFGAN) is proposed. Time-domain waveforms are firstly transformed into the time-frequency diagrams to highlight the fault characteristics. Subsequently, a local fusion module is used to fully utilize extremely limited samples and fuse the local features. Finally, a new generator embedded with multi-head attention modules is constructed to effectively improve the accuracy and flexibility of the feature fusion process. The proposed method is applied to the analysis of planetary gearbox vibration signals. The results show that the proposed method can generate a large number of samples with higher similarity and better diversity compared with the existing mainstream GANs using 6 training samples in each type. The generated samples are used to augment the limited dataset, prominently improving the accuracy of the fault diagnosis task

Clustering-guided novel unsupervised domain adversarial network for partial transfer fault diagnosis of rotating machinery

Unsupervised partial transfer fault diagnosis studies of rotating machinery have practical significance, which still exists some challenges, for example, the learned domain-specific statistics and parameters usually influence the learning effect of target-domain features to some degree, and the relatively scattered target-domain features will lead to negative transfer. To overcome those limitations and further improve partial transfer fault diagnosis performance, a clustering-guided novel unsupervised domain adversarial network is proposed in this paper. Firstly, a novel unsupervised domain adversarial network is constructed using domain-specific batch normalization to remove domain-specific information to enhance alignment between source and target domains. Secondly, embedded clustering strategy is designed to learn tightly clustered target-domain features to suppress negative transfer in partial domain adaptation process. Finally, a joint optimization objective function is defined to balance different losses to improve the training and diagnosis performance. Two experimental cases of bevel gearbox and bearing are used to validate the effectiveness and superiority of the proposed method in solving unsupervised partial transfer fault diagnosis problems

Modified Stacked Autoencoder Using Adaptive Morlet Wavelet for Intelligent Fault Diagnosis of Rotating Machinery

Intelligent fault diagnosis techniques play an important role in improving the abilities of automated monitoring, inference, and decision making for the repair and maintenance of machinery and processes. In this article, a modified stacked autoencoder (MSAE) that uses adaptive Morlet wavelet is proposed to automatically diagnose various fault types and severities of rotating machinery. First, the Morlet wavelet activation function is utilized to construct an MSAE to establish an accurate nonlinear mapping between the raw nonstationary vibration data and different fault states. Then, the nonnegative constraint is applied to enhance the cost function to improve sparsity performance and reconstruction quality. Finally, the fruit fly optimization algorithm is used to determine the adjustable parameters of the Morlet wavelet to flexibly match the characteristics of the analyzed data. The proposed method is used to analyze the raw vibration data collected from a sun gear unit and a roller bearing unit. Experimental results show that the proposed method is superior to other state-of-the-art methods

A Stacked GRU-RNN-based Approach for Predicting Renewable Energy and Electricity Load for Smart Grid Operation

Predictions of renewable energy (RE) generation and electricity load are critical to smart grid operation. However, the prediction task remains challenging due to the intermittent and chaotic character of RE sources, and the diverse user behavior and power consumers. This paper presents a novel method for the prediction of RE generation and electricity load using improved stacked gated recurrent unit-recurrent neural network (GRU-RNN) for both uni-variate and multi-variate scenarios. First, multiple sensitive monitoring parameters or historical electricity consumption data are selected according to the correlation analysis to form the input data. Second, a stacked GRU-RNN using a simplified GRU is constructed with improved training algorithm based on AdaGrad and adjustable momentum. The modified GRU-RNN structure and improved training method enhance training efficiency and robustness. Third, the stacked GRU-RNN is used to establish an accurate mapping between the selected variables and RE generation or electricity load due to its self-feedback connections and improved training mechanism. The proposed method is verified by using two experiments: prediction of wind power generation using multiple weather parameters and prediction of electricity load with historical energy consumption data. The experimental results demonstrate that the proposed method outperforms state-of-the-art methods of machine learning or deep learning in achieving an accurate energy prediction for effective smart grid operation

Molecular doping enabled scalable blading of efficient hole-transport-layer-free perovskite solar cells

The efficiencies of perovskite solar cells (PSCs) are now reaching such consistently high levels that scalable manufacturing at low cost is becoming critical. However, this remains challenging due to the expensive hole-transporting materials usually employed, and difficulties associated with the scalable deposition of other functional layers. By simplifying the device architecture, hole-transport-layer-free PSCs with improved photovoltaic performance are fabricated via a scalable doctor-blading process. Molecular doping of halide perovskite films improved the conductivity of the films and their electronic contact with the conductive substrate, resulting in a reduced series resistance. It facilitates the extraction of photoexcited holes from perovskite directly to the conductive substrate. The bladed hole-transport-layerfree PSCs showed a stabilized power conversion efficiency above 20.0%. This work represents a significant step towards the scalable, cost-effective manufacturing of PSCs with both high performance and simple fabrication processes

Eyes grow towards mild hyperopia rather than emmetropia in Chinese preschool children

Purpose: To document one-year changes in refraction and refractive components in preschool children. Methods: Children, 3–5 years old, in the Jiading District, Shanghai, were followed for one year. At each visit, axial length (AL), refraction under cycloplegia (1% cyclopentolate), spherical dioptres (DS), cylinder dioptres (DC), spherical equivalent refraction (SER) and corneal curvature radius (CR) were measured. Results: The study included 458 right eyes of 458 children. The mean changes in DS, DC and SER were 0.02 ± 0.35 D, −0.02 ± 0.33 D and 0.01 ± 0.37 D, while the mean changes in AL, CR and lens power (LP) were 0.27 ± 0.10 mm, 0.00 ± 0.04 mm and − 0.93 ± 0.49 D. The change in the SER was linearly correlated with the baseline SER (coefficient = −0.147, p < 0.001). When the baseline SER was at 1.05 D (95% CI = 0.21 to 2.16), the change in SER was 0 D. The baseline SER was also linearly associated with the change in LP (coefficient = 0.104, p = 0.013), but not with the change in AL (p = 0.957) or with the change in CR (p = 0.263). Conclusion: In eyes with a baseline SER less than +1.00 D, LP loss was higher compared to axial elongation, leading to hyperopic shifts in refraction, whereas for those with baseline SER over this range, loss of LP compared to axial elongation was reduced, leading to myopic shifts. This model indicated the homeostasis of human refraction and explained how refractive development leads to a preferred state of mild hyperopia.The study was funded by Chinese National NatureScience Foundation (No. 81670898), Chinese Nat-ural Science Foundation for Young Staff (No.81800881), The Shanghai Three Year Public HealthAction Program (No. GWIV-3.3), The ShanghaiHigh-level Oversea Training Team Program on EyePublic Health (No. GWTD2015S08), The ShanghaiOutstanding Academic Leader Program (No.16XD1402300), Shanghai Nature Science Founda-tion (NO. 15ZR1438400), Three-year Action Pro-gram of Shanghai Municipality for Strengtheningthe Construction of the Public Health System(NO.GWIV-13.2), Key Discipline of PublicHealth-Eye health in Shanghai (No.15GWZK0601), Municipal Human ResourcesDevelopment Program for Outstanding YoungTalents in Medical and Health Sciences in Shanghai(Grant No. 2017YQ019), Shanghai Sailing Program(No. 17YF1416100), Foundation of ShanghaiMunicipal Commission of Health and FamilyPlanning (No. 20184Y0217), National Key R&DProgramofChina(2016YFC0904800,2019YFC0840607), National Science and Technol-ogy Major Project of China (2017ZX09304010) andSongjiang Science Foundation (No. 19SJKJGG30)