Fault Diagnosis of Rotating Equipment Bearing Based on EEMD and Improved Sparse Representation Algorithm

Aiming at the problem that the vibration signals of rolling bearings working in a harsh environment are mixed with many harmonic components and noise signals, while the traditional sparse representation algorithm takes a long time to calculate and has a limited accuracy, a bearing fault feature extraction method based on the ensemble empirical mode decomposition (EEMD) algorithm and improved sparse representation is proposed. Firstly, an improved orthogonal matching pursuit (adapOMP) algorithm is used to separate the harmonic components in the signal to obtain the filtered signal. The processed signal is decomposed by EEMD, and the signal with a kurtosis greater than three is reconstructed. Then, Hankel matrix transformation is carried out to construct the learning dictionary. The K-singular value decomposition (K-SVD) algorithm using the improved termination criterion makes the algorithm have a certain adaptability, and the reconstructed signal is constructed by processing the EEMD results. Through the comparative analysis of the three methods under strong noise, although the K-SVD algorithm can produce good results after being processed by the adapOMP algorithm, the effect of the algorithm is not obvious in the low-frequency range. The method proposed in this paper can effectively extract the impact component from the signal. This will have a positive effect on the extraction of rotating machinery impact features in complex noise environments

DeepC2: AI-powered Covert Botnet Command and Control on OSNs

Botnets are one of the major threats to computer security. In previous botnet command and control (C&C) scenarios using online social networks (OSNs), methods for addressing (e.g., IDs, links, or DGAs) are hardcoded into bots. Once a bot is reverse engineered, the botmaster and C&C infrastructure will be exposed. Additionally, abnormal content from explicit commands may expose botmasters and raise anomalies on OSNs. To overcome these deficiencies, we proposed DeepC2, an AI-powered covert C&C method on OSNs. By leveraging neural networks, bots can find botmasters by avatars, which are converted into feature vectors and embedded into bots. Adversaries cannot infer botmasters' accounts from the vectors. Commands are embedded into normal contents (e.g., tweets and comments) using text data augmentation and hash collision. Experiments on Twitter show that command-embedded contents can be generated efficiently, and bots can find botmasters and obtain commands accurately. Security analysis on different scenarios show that DeepC2 is robust and hard to be shut down. By demonstrating how AI may help promote covert communication on OSNs, this work provides a new perspective on botnet detection and confrontation.Comment: 13 pages, 15 figures, 7 tables. Discussion on possible countermeasures update

PO-067 Effects of oral Lycium barbarum juice in red blood cell antioxidant biomarkers and physical function during 8 days of aerobic exercise

Objective Lycium barbarum polysaccharide (LBP) is the main active components of Lycium barbarum, its benefits to anti-aging, vision, kidney, and liver functions. Nevertheless, there is still a scarcity of experimental evidence to support the effect of Lycium barbarum on aerobic exercise.This a randomized controlled trial was observed the effects of oral Lycium barbarum juice in red blood cell antioxidant biomarkers and physical function during 8 days of aerobic exercise. Methods 28 healthy male college students were divided into control group（16）and experimental group（12），and underwent interval running once every other day，total of 8 days. Exercise program: An exercise session includes two 30-minute aerobic exercises at 60%VO2max and a five-minute break. For the duration of the 8 days period, participants exercised one time every other day and the experimental group drank 100ml Lycium barbarum juice (each LBP content 360-440mg%) at bedtime every night. In ninth days, all the experimenters did exhaustive exercise with 80%VO2max on a treadmill with 8°.simultaneous recording of motion duration. The levels of red blood cell SOD, MDA, GSH-PX, serum CAT, serum TAC and other oxygenation stress markers and BLA, Glu, Urea, CK, Urine eight items and other physical function indexes of the subjects were determined before the experiment and after the completion of each intensity exercise. Differences between before and after intervention values were tested using a paired t test.And to compare the mean of outcomes in quantitative variables between the 2 groups, a independent t-test was used. The SPSS software (version 17, SPSS Inc, Chicago, IL, USA) was applied for data analysis and statistical significance was accepted at P < 0.05. Results (1)After 8 days of oral Lycium barbarum juice, the exhaustion time of exercise force in the experimental group was 30.76 ±12.19min, while the control group was 23.64±8.56min. Compared with the control group, the average exercise exhaustion time of the experimental group was prolonged 7.12min. (2)The red blood cell SOD in the two groups after 8 days of aerobic exercise had significant and significant improvement (P < 0.05, P < 0.01), and moreover, the increase of the experimental group was significantly higher than that of the control group (P < 0.05).As well as, the blood erythrocyte GSH-PX and serum TAC were significantly enhanced after the experiment (P < 0.01).It is suggested that increasing the levels of SOD and GSH-PX in vivo is beneficial in scavenging the free radicals produced by body movement. (3)After the 8 days oral Lycium barbarum juice, the decrease of MDA in blood red blood cells in the experimental group was greater than that of the control group (P < 0.01), indicating that the juice of Lycium barbarum could reduce the production of lipid peroxide products. (4) Exhaustion exercise after 8 days of oral Lycium barbarum juice, the physical function indexes of the experimental group, such as BLA, Urea, and CK were reduced. The positive rate of eight urine items was less than that in the control group, 8 in the control group, 2 for bilirubin positive, 3 in the urinary occult blood and 5 in the urine protein, while only 1 in the experimental group were positive for urine protein.   Conclusions Oral Lycium barbarum juice can improve the activity of antioxidant enzymes during aerobic exercise, reduce the formation of lipid peroxides in the body, protect the biological function of red blood cells, improve the physical function and postpone the production of sports fatigue

OR-023 Physical evaluation of 6-7 years old female preselected tennis players

Objective Through testing and analysis the characteristics of body shape，body composition，bone growth and physical fitness，hemoglobin, testosterone of 6-7 years old female preselected tennis players，the study aim was to provide reference bases for the early selection of female tennis players. Methods A total of 75 female preselected tennis players(initial selection by the coaches) aged from 6 to 7 years were came from Hebei, Hubei, Qinghai and Inner Mongolia province, who came to Research Center for Heath related Physical Fitness Evaluation of Guangzhou Sport University for physical fitness test from July 2016 to July 2018. The height, weight, length of upper limbs, length of lower limbs, iliac width, shoulder width, body fat, muscle mass, bone age，bone mass density(BMD), anaerobic power and PWC170, reaction time, vertical jump, grip strength, hemoglobin, testosterone were measured using related instruments and methods, and calculated derived indicators BMI, iliac width/shoulder width. Data were compared with the national standard of physical health of students and/or evaluated by deviation method，and correlation had been analysed among physical parameters. Results 1)The 75 female preselected tennis players’ aged from 6 to 7 years height and weight were 128.10±5.32cm and 25.70±3.87kg，and there are 47 girls height upper medium grade level, 60% of which weight was at a moderate level, their BMI were 15.48±1.50kg/m2，and all in the normal range, iliac width/shoulder width ×100 was 76.52±7.00, 70.7% 0f which was above medium grade level, the upper and lower limbs were 54.28±3.60cm and 71.68±5.26cm, girls’ PBF were 21.03±6.44, muscle weight were 18.94±3.00kg, BMD were 2.04±2.20, and no low bone strength were fund; Anaerobic power of all female preselected tennis players were 135.93±31.65kg.cm, and the values of the PWC170 relative weight were 10.79±2.56kg.m/min.kg, reaction time were 0.628±0.128s, vertical jump were 21.13±4.95cm, the grip of right and left hand were 10.36±2.15kg and 10.06±2.40kg, the physical fitness parameters above in the upper middle class were more girls than the lower middle class; The hemoglobin content was 132.15±8.70g/L, which was above the normal level (110 -160g/L), the serum testosterone concentration was 1.52±1.20umol/L, which was much higher than that of normal girls (0-0.7umol/L). 2) When age was controlled, there was negative correlation between T and PFB, vertical jump and body weight, PWC170 and reaction time(P＜0.05), and there was positive correlation between hemoglobin and muscle weight(P＜0.05), height, and vertical jump(P＜0.01), muscle weight and anaerobic power(P<0.01)，anaerobic power and height，weight，BMI，upper and lower limbs(P<0.01, P<0.01, P<0.01, P<0.05, P<0.01)，PWC170 and vertical jump，the grip of right and left hand(P<0.01, P<0.05, P<0.05), vertical jump and upper, lower limbs, iliac width/shoulder width(P<0.01, P<0.01, P<0.01). Conclusions 75 female aged from 6 to 7 years old preselected tennis players’ body shape, physical fitness, physiological and biochemical function were superior to peers, and in those parameters，there were more people in upper middle grade than  lower middle grade. There is a certain correlation between body composition, shape and fitness of female preselected tennis players’ aged from 6 to 7 years

EARLY FAULT DIAGNOSIS OF ROLLING BEARING BASED ON WAVELET PACKET TRANSFORM ADAPTIVE TEAGER ENERGY SPECTRUM

Considering the early fault feature information of rolling bearings is difficult to identify,and form the frequency bands after wavelet packet decomposition can not be effectively determined and adaptive to extract the resonance band,the concept of amplitude entropy of frequency band is proposed.On this basis,the wavelet packet transform and Teager energy spectrum was combined,a rolling bearing early fault feature extraction method is proposed based on wavelet packet transform adaptive Teager energy spectrum.Firstly,the vibration signal was decomposed by wavelet packet,and the frequency amplitude entropy of each subband was calculated.Then,on the basis of kurtosis index to determine the best entropy and the optimal decomposition level of wavelet packet,thus,the resonance band was extracted adaptively and effectively.Finally,the Teager energy spectrum analysis was performed to identify the frequency of the bearing fault.Through the signal simulation and experimental data analysis it verifies the effectiveness of the proposed method

Federated Learning Based Fault Diagnosis Driven by Intra-Client Imbalance Degree

Federated learning is an effective means to combine model information from different clients to achieve joint optimization when the model of a single client is insufficient. In the case when there is an inter-client data imbalance, it is significant to design an imbalanced federation aggregation strategy to aggregate model information so that each client can benefit from the federation global model. However, the existing method has failed to achieve an efficient federation strategy in the case when there is an imbalance mode mismatch between clients. This paper aims to design a federated learning method guided by intra-client imbalance degree to ensure that each client can receive the maximum benefit from the federation model. The degree of intra-client imbalance, measured by gain of a class-by-class model update on the federation model based on a small balanced dataset, is used to guide the designing of federation strategy. An experimental validation for the benchmark dataset of rolling bearing shows that a 23.33% improvement of fault diagnosis accuracy can be achieved in the case when the degree of imbalance mode mismatch between clients is prominent

Two new species of Perenniporia (Polyporales, Basidiomycota)

Two new species of Perenniporia, P. pseudotephropora sp. nov. and P. subcorticola sp. nov., are introduced respectively from Brazil and China based on morphological characteristics and molecular data. Perenniporia pseudotephropora is characterised by perennial, pileate basidiocarps with distinctly stratified tubes, grey pores, tissues becoming dark in KOH, a dimitic hyphal system with slightly dextrinoid arboriform skeletal hyphae and broadly ellipsoid to subglobose, truncate, weakly dextrinoid, cyanophilous basidiospores, measuring 4.9–5.2 × 4–4.8 μm. Perenniporia subcorticola is characterised by resupinate basidiocarps, yellow pores with thick dissepiments, tissues becoming dark in KOH, flexuous skeletal hyphae, ellipsoid, truncate and slightly dextrinoid basidiospores, measuring 4.2–5 × 3.5–4.2 µm. The morphologically-similar species and phylogenetically closely-related species to the two new species are discussed

Multi-Scale Recursive Semi-Supervised Deep Learning Fault Diagnosis Method with Attention Gate

The efficiency of deep learning-based fault diagnosis methods for bearings is affected by the sample size of the labeled data, which might be insufficient in the engineering field. Self-training is a commonly used semi-supervised method, which is usually limited by the accuracy of features for unlabeled data screening. It is significant to design an efficient training mechanism to extract accurate features and a novel feature fusion mechanism to ensure that the fused feature is capable of screening. A novel training mechanism of multi-scale recursion (MRAE) is designed for Autoencoder in this article, which can be used for accurate feature extraction with a small amount of labeled data. An attention gate-based fusion mechanism was constructed to make full use of all useful features in the sense that it can incorporate distinguishing features on different scales. Utilizing large numbers of unlabeled data, the proposed multi-scale recursive semi-supervised deep learning fault diagnosis method with attention gate (MRAE-AG) can efficiently improve the fault diagnosis performance of DNNs trained by a small number of labeled data. A benchmark dataset from the Case Western Reserve University bearing data center was used to validate this novel method which shows that 7.76% accuracy improvement can be achieved in the case when only 10 labeled samples was available for supervised training of the DNN-based fault diagnosis model