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Along with the number and the functional complexity of machines increase in the intelligent manufacturing system, the probability of faults will increase, which may lead to huge economic losses. Traditional passive or regular maintenance methods of solving the faults have the problems of low efficiency and huge resource consumption. Besides, traditional maintenance methods mostly contain single model, so all the prognostics and maintenance tasks of the intelligent manufacturing system can hardly be addressed at the same time. Therefore, this paper proposes a novel predictive maintenance (PDM) method based on the improved deep adversarial learning (LSTM-GAN). The long-short-term memory (LSTM) network can solve the disadvantage of vanishing gradients and the mode collapse from the generative adversarial network (GAN). The method can not only avoid the mode collapse of GAN but also realize the self-detection of abnormal data. Meanwhile, the predictive maintenance model includes two prediction models and a maintenance decision model. The prediction models can predict the state of the machine and the fault of the machine in advance. Then the maintenance decision model will arrange maintenance personnel and offer a plan of maintenance. Finally, a case study about predictive maintenance using LSTM-GAN in the intelligent manufacturing system is presented. The fault prediction accuracy of LTSM-GAN is as high as 99.68%. With the comparison between LSTM-GAN and other traditional methods, LSTM-GAN shows priority both in accuracy and efficiency. Moreover, the proposed PDM can reduce maintenance costs and downtime so that the life of machines in the intelligent manufacturing system will extend.

Coronary arteriongraphy (CAG) is an accurate invasive technique for the diagnosis of coronary heart disease (CHD). However, its invasive procedure is not appropriate for the detection of CHD in the annual physical examination. With the successful application of machine learning (ML) in various fields, our goal is to perform selective integration of multiple ML algorithms and verify the validity of feature selection methods with personal clinical information commonly seen in the annual physical examination. In this study, a two level stacking based model is designed in which level 1 is base-level and level 2 is meta-level. The predictions of base-level classifiers is selected as the input of meta-level. The pearson correlation coefficient and maximum information coefficient are first calculated to find the classifier with the lowest correlation. Then enumeration algorithm is used to find the best combining classifiers which acquire the best result in the end. The Z-Alizadeh Sani CHD dataset which we use consists of 303 cases verified by CAG. Experimental results demonstrate that the proposed model obtains an accuracy, sensitivity and specificity of 95.43%, 95.84%, 94.44%, respectively for the detection of CHD. The proposed method can effectively aid clinicians to detect those with normal coronary arteries from those with CHD.

The depth development and widespread application of edge intelligence technology based on the Internet of Things has led to edge-cloud collaboration and related research. In recent years, with the rapid development of the Internet of Things and the formation of super-city groups, the management characteristics of enterprises with multiple manufacturing plants served for headquarters have become increasingly obvious. The problem of order dynamic fluctuations caused by personalized customization requirements has become more prominent, which makes it impossible to do global long-period prediction or real-time short-period response relied solely on the cloud or edge. Therefore, this paper proposes a production system scheduling framework under the edge-cloud collaborative paradigm based on the dynamic fluctuation of orders under these background, and builds an edge-cloud collaborative scheduling model, which guarantees real-time distributed scheduling at the edge. It enabled the cloud to periodically predict the total completion time of production tasks at the headquarters based on the value-added data uploaded by the edge, and to support more accurate and efficient scheduling at the edge based on the prediction results. Finally, an example analysis proved the rationality of the scheduling mechanism and the effectiveness of the scheduling model. The proposed method can provide a certain reference for task scheduling in the edge-cloud collaborative production paradigm.

There is a growing interest in automated diagnosis of coronary artery disease (CAD) with the application of machine learning (ML) methods to the body surface electrocardiograph (ECG). Although prior studies have documented associations of CAD with increased QT variability and ST-T segment abnormalities such as T-wave inversion and ST-segment elevation or depression, their efficacy in automated CAD detection has not been fully investigated. To validate their usefulness, a dataset containing related clinical characteristics and 5-min single-lead ECGs of 107 healthy controls and 93 CAD patients was first constructed. Based on this dataset, simultaneous analyses were then conducted in five scenarios, in which different ML algorithms were applied to classify the two groups with various features derived from the RR and QT interval time-series and ST-T segment waveforms. Compared with utilizing features obtained from the RR interval time-series, better classification results were achieved utilizing that obtained from the QT interval time-series. The classification results were elevated with combining utilization of features derived from both the RR and QT interval time-series. By further fusing features extracted from ST-T segment waveforms, the best performance was achieved with 96.16% accuracy, 95.75% sensitivity, and 96.40% specificity. Based the best performance, an automated CAD detection system was developed with extreme gradient boosting, an ensemble ML algorithm, and the residual neural network, namely, a deep learning method. The results of this study support the potential of information derived from the QT interval time-series and ST-T segment waveforms in ECG-based automated CAD detection.

Heart rate variability (HRV), systolic period variability (SPV), and diastolic period variability (DPV) have shown potential for assessing cardiac function. It is unknown whether the time delay between the myocardial electrical and mechanical activities (i.e., electromechanical delay, EMD) also possesses variability, and if it does, whether this EMD variability (EMDV) could render additional value for cardiac function assessment. In this paper, we extracted the beat-to-beat EMD from 5-min simultaneously recorded electrocardiogram and phonocardiogram signals in 30 patients with coronary artery disease (CAD) and 30 healthy control subjects, and studied its variability using the same methods as applied for HRV including time-domain measures [mean and standard deviation (SD)], frequency-domain measures [normalized low- and high-frequency (LFn, HFn) and LF/HF)], and nonlinear measures [sample entropy (SampEn), permutation entropy (PE), and dynamical patterns]. In addition, we examined whether the addition of EMDV could offer improved performance for distinguishing between the two groups compared to using the HRV, SPV, and DPV features. Support vector machine with 10-fold cross-validation was used for classification. Results showed increased SD of SPV, increased mean, SD and decreased SampEn of EMDV in CAD patients. Besides, the dynamical pattern analysis showed that CAD patients had significantly increased fluctuated patterns and decreased monotonous patterns in EMDV. In particular, the addition of EMDV indices dramatically increased the classification accuracy from 0.729 based on HRV, SPV, and DPV features to 0.958. Our results suggest promising of the EMDV analysis that could potentially be helpful for detecting CAD noninvasively.

Physiological signal variability can offer important insight into cardiovascular activity and clinical cardiovascular diseases. Heart rate variability (HRV) and pulse transit time variability (PTTV) are two important time series variabilities. However, combining HRV and PTTV can enhance the classification accuracy for heart failure which is unknown. In this paper, a simultaneous analysis of HRV and PTTV performed on both normal subjects and heart failure patients, was carried out, aiming to investigate the improvement of HRV-based heart failure detection with the assistant of PTTV analysis. Forty normal subjects and forty heart failure patients were enrolled. Standard limb lead-II electrocardiogram and radial artery pressure waveforms were synchronously recorded. HRV and PTTV analysis were performed on the acquired RR and PTT time series using the standard time- (MEAN, SDNN, and RMSSD), frequency- (LF, HF, and LF/HF), and non-linear (SD1, SD2, sample entropy, and fuzzy measure entropy) domain indices. The results showed that all HRV indices except MEAN (P = 0.1) and LF/HF (P = 0.9) showed significant differences (all P

Electrocardiogram (ECG) and phonocardiogram (PCG) signals reflect the electrical and mechanical activities of the heart, respectively. Although studies have documented that some abnormalities in ECG and PCG signals are associated with coronary artery disease (CAD), only few researches have combined the two signals for automatic CAD detection. This paper aims to differentiate between CAD and non-CAD groups using simultaneously collected ECG and PCG signals. To entirely exploit the underlying information in these signals, a novel dual-input neural network that integrates the feature extraction and deep learning methods is developed. First, the ECG and PCG features are extracted from multiple domains, and the information gain ratio is used to select important features. On the other hand, the ECG signal and the decomposed PCG signal (at four scales) are concatenated as a five-channel signal. Then, the selected features and the five-channel signal are fed into the proposed network composed of a fully connected model and a deep learning model. The results show that the classification performance of either feature extraction or deep learning is insufficient when using only ECG or PCG signal, and combining the two signals improves the performance. Further, when using the proposed network, the best result is obtained with accuracy, sensitivity, specificity, and G-mean of 95.62%, 98.48%, 89.17%, and 93.69%, respectively. Comparisons with existing studies demonstrate that the proposed network can effectively capture the combined information of ECG and PCG signals for the recognition of CAD.

Image dehazing is a technique to enhance the images acquired in poor weather conditions, such as fog and haze. Existing image dehazing methods are mainly based on dark channel prior. Since the dark channel is not reasonable for sky regions, a sky segmentation and region wised medium transmission based image dehazing method is proposed in this paper. Firstly, sky regions are segmented by quad-tree splitting based feature pixels detection. Then, a medium transmission estimation method for sky regions is proposed based on color characteristic observation of sky regions. The medium transmission is then filtered by an edge preserving guided filter. Finally, based on the estimated medium transmission, the hazed images are restored. Experimental results demonstrate that the performance of the proposed method is better than that of existing methods. The restored image is more natural, especially in the sky regions.