A Spatiotemporal Probabilistic Graphical Model Based on Adaptive Expectation-Maximization Attention for Individual Trajectory Reconstruction Considering Incomplete Observations.

Spatiotemporal information on individual trajectories in urban rail transit is important for operational strategy adjustment, personalized recommendation, and emergency command decision-making. However, due to the lack of journey observations, it is difficult to accurately infer unknown information from trajectories based only on AFC and AVL data. To address the problem, this paper proposes a spatiotemporal probabilistic graphical model based on adaptive expectation maximization attention (STPGM-AEMA) to achieve the reconstruction of individual trajectories. The approach consists of three steps: first, the potential train alternative set and the egress time alternative set of individuals are obtained through data mining and combinatorial enumeration. Then, global and local potential variables are introduced to construct a spatiotemporal probabilistic graphical model, provide the inference process for unknown events, and state information about individual trajectories. Further, considering the effect of missing data, an attention mechanism-enhanced expectation-maximization algorithm is proposed to achieve maximum likelihood estimation of individual trajectories. Finally, typical datasets of origin-destination pairs and actual individual trajectory tracking data are used to validate the effectiveness of the proposed method. The results show that the STPGM-AEMA method is more than 95% accurate in recovering missing information in the observed data, which is at least 15% more accurate than the traditional methods (i.e., PTAM-MLE and MPTAM-EM).

Difficulties faced by intensive care nurses in caring for patients with delirium: A cross-sectional, multicentre study.

BACKGROUND: Intensive care nurses experience many difficulties in caring for patients with delirium. Thus, it is valuable to conduct in-depth research on the factors that influence the difficulties faced by intensive care nurses in caring for those with delirium as doing so can result in tangible improvements in patient outcomes. OBJECTIVES: The objective of this study was to explore the difficulties faced by intensive care nurses in caring for patients with delirium in light of the demographic, clinical, and professional and management characteristics of nurses. METHODS: A cross-sectional study involving 360 intensive care nurses from eight general hospitals in Taizhou, Zhejiang Province, China. The participants completed questionnaires assessing the level of difficulty they faced in caring for patients with delirium and their level of delirium-related knowledge. RESULTS: The highest overall mean scores on the difficulty scale subscales were observed for ensuring safety (2.92 ± 0.30), dealing with stress and distress (2.80 ± 0.37), and lack of resources (2.85 ± 0.41). The main factors influencing nurses' difficulty in caring for these patients were title, status as a critical care specialist nurse, training regarding delirium, a standardised delirium management process, the knowledge level regarding delirium, the total number of years working in the intensive care unit, and work communication ability. Likewise, most of these characteristics made it difficult for the nurses to use delirium screening tools. CONCLUSIONS: This study provides insights into factors influencing the difficulties faced by intensive care nurses in caring for patients with delirium and in using delirium screening tools. Our findings suggested that nursing managers could develop targeted improvement strategies and provide more resources to support nurses, thereby improving the quality of delirium care and patient outcomes by using the results from this study. These findings can also provide evidence to support intervention studies in the future.

Efficient Adsorption and Extraction of Glutathione S-Transferases with Glutathione-Functionalized Graphene Oxide-Polyhedral Oligomeric Silsesquioxane Composite.

Glutathione S-transferases (GSTs) are important type-II detoxification enzymes that protect DNA and proteins from damage and are often used as protein tags for the expression of fusion proteins. In the present work, octa-aminopropyl caged polyhedral oligomeric silsesquioxane (OA-POSS) was prepared via acid-catalyzed hydrolysis of 3-aminopropyltriethoxysilane and polymerized on the surface of graphene oxide (GO) through an amidation reaction. Glutathione (GSH) was then modified to GO-POSS through a Michael addition reaction to obtain a GSH-functionalized GO-POSS composite (GPG). The structure and characteristics of the as-prepared GPG composite were characterized using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), thermogravity analysis, and surface charge analysis. The specific binding interactions between glutathione and GST gave GPG favorable adsorption selectivity towards GST, and other proteins did not affect GST adsorption. The adsorption behavior of GST on the GPG composite conformed to the Langmuir isotherm model, and the adsorption capacity of GST was high up to 364.94 mg g-1 under optimal conditions. The GPG-based solid-phase adsorption process was applied to the extraction of GST from a crude enzyme solution of pig liver, and high-purity GST was obtained via SDS-PAGE identification.

Incomplete pythagorean fuzzy preference relation for subway station safety management during COVID-19 pandemic.

Completing the Pythagorean fuzzy preference relations (PFPRs) based on additive consistency may exceed the defined domain. Therefore, we develop a group decision-making (GDM) method with incomplete PFPRs. Firstly, sufficient conditions for the expressibility of estimated preference values in PFPRs based on additive consistency are presented. Next, the correction algorithm is developed to correct the inexpressible elements in incomplete PFPRs. Then, a GDM method based on incomplete PFPRs is proposed to determine the objective weights of decision-makers. Finally, an example of subway station safety management during COVID-19 is selected to illustrate the applicability of the developed GDM method. The results show that the developed GDM method effectively identifies the crucial risk factor in subway station safety management and has better performance in terms of computational time complexity than the multiplicative consistency method.

Automatic Defect Description of Railway Track Line Image Based on Dense Captioning.

The state monitoring of the railway track line is one of the important tasks to ensure the safety of the railway transportation system. While the defect recognition result, that is, the inspection report, is the main basis for the maintenance decision. Most previous attempts have proposed intelligent detection methods to achieve rapid and accurate inspection of the safety state of the railway track line. However, there are few investigations on the automatic generation of inspection reports. Fortunately, inspired by the recent advances and successes in dense captioning, such technologies can be investigated and used to generate textual information on the type, position, status, and interrelationship of the key components from the field images. To this end, based on the work of DenseCap, a railway track line image captioning model (RTLCap for short) is proposed, which replaces VGG16 with ResNet-50-FPN as the backbone of the model to extract more powerful image features. In addition, towards the problems of object occlusion and category imbalance in the field images, Soft-NMS and Focal Loss are applied in RTLCap to promote defect description performance. After that, to improve the image processing speed of RTLCap and reduce the complexity of the model, a reconstructed RTLCap model named Faster RTLCap is presented with the help of YOLOv3. In the encoder part, a multi-level regional feature localization, mapping, and fusion module (MFLMF) are proposed to extract regional features, and an SPP (Spatial Pyramid Pooling) layer is employed after MFLMF to reduce model parameters. As for the decoder part, a stacked LSTM is adopted as the language model for better language representation learning. Both quantitative and qualitative experimental results demonstrate the effectiveness of the proposed methods.

A Hybrid Rule-Based and Data-Driven Approach to Illegal Transshipment Identification with Interpretable Behavior Features.

Illegal transshipment of maritime ships is usually closely related to illegal activities such as smuggling, human trafficking, piracy plunder, and illegal fishing. Intelligent identification of illegal transshipment has become an important technical means to ensure the safety of maritime transport. However, due to different geographical environments, legal policies and regulatory requirements in each sea area, there are differences in the movement characteristics and geographical distribution of illegal transshipment behavior in different time and space. Moreover, in areas with dense traffic flow, normal navigation behavior can easily be identified as illegal transshipment, resulting in a high rate of misidentification. This paper proposes a hybrid rule-based and data-driven approach to solve the problem of missing identification in fixed threshold methods and introduces a traffic density feature to reduce the misidentification rate in dense traffic areas. The method is both interpretable and adaptable through unsupervised clustering to get suitable threshold distribution combination for regulatory sea areas. The evaluation results in two different sea areas show that the proposed method is applicable. Compared with other widely used identification methods, this method identifies more illegal transshipment events, which are highly suspicious, and gives warning much earlier. The proposed method can even filter out misidentification events from compared methods' results, which account for more than half of the total number.

Improving robustness in interdependent networks under intentional attacks by optimizing intra-link allocation.

The interdependent network is particularly vulnerable to attacks on high degree nodes; therefore, the improvement of its robustness under intentional attacks has become an important topic. In this paper, we put forward a new metric to quantify the robustness of interdependent networks against intentional attacks and develop an improved simulated annealing algorithm (ISAA) to maximize this metric by optimizing the allocation of intra-links in subnetworks. Based on the comparison between the ISAA and existing algorithms, it is found that the algorithm presented in this paper is more effective to enhance the robustness of an interdependent scale-free network (ISFN). By applying the ISAA to ISFNs with different coupling preferences, there is a key finding that the robustness of the optimized ISFN is significantly stronger than that of the original ISFN. In particular, for cases of disassortative and random couplings, no sudden collapse occurs in optimized ISFNs. According to the analysis of the degree and the clustering coefficient, we find that the subnetwork of the optimized ISFN exhibits an onion-like structure. In addition, the ISFN whose robustness is enhanced to resist the attacks on high degree nodes is still robust to the intentional attacks concerning the betweenness and PageRank.

Analgesic Pump Tubing Securement to Prevent Dislodgement of Peripheral Vein Indwelling Catheter.

PURPOSE: This study aims to compare the incidence of complications when using a new approach to secure an indwelling peripheral venous catheter (PVC), involving tying of the tube with a surgical knot at two places and several layers of elastic adhesive bandage, with a standard approach using sterile, transparent, and protective film. METHODS: This study enrolled 311 consecutive adults undergoing thoracoscopic lobectomy under general anesthesia at Taizhou Hospital of Zhejiang Province between October 2017 and May 2018. Patients were randomized to experimental and control groups and were followed for up to 72 hours. The primary endpoint was dislodgement of the PVC. Secondary endpoints were blood in the catheter; analgesia pump obstruction alarm; time taken and cost of PVC replacement; replacement of securing materials and analgesia pump line; and time and cost of replacing them. All adverse events were recorded. FINDINGS: Final analysis included 248 patients (experimental group: n = 126; control group: n = 122). PVC dislodgement was less frequent in the experimental group than in the control group. In the control group, 78.7% of patients required replacement of securing materials (costing 37 cents each time) and 13.1% required PVC replacement (costing 3.6 dollars each time), necessitating additional nursing time. No patients in the experimental group required replacement of the PVC or securing materials. Blisters were less common in the experimental group than in the control group (0% vs 9.84%, P < .001). No patients had limb edema. CONCLUSIONS: This new method of securing an analgesia pump line can reduce traction on the indwelling PVC, lowering the dislodgement rate.

Expression and prognostic significance of the P53-related DNA damage repair proteins checkpoint kinase 1 (CHK1) and growth arrest and DNA-damage-inducible 45 alpha (GADD45A) in human oral squamous cell carcinoma.

DNA damage repair is a key factor in the maintenance of cell genome stability, plays an important role in the regulation of tumour evolution, and can affect the prognosis of cancer patients. This study aimed to detect the protein expression of the DNA damage repair protein P53 and its upstream and downstream regulators, CHK1, GADD45A, and MDM2, in oral squamous cell carcinoma (OSCC), in order to analyse the association between the expression of these proteins and overall survival, and to assess their prognostic implications for OSCC patients. The expression of the above proteins was detected by immunohistochemistry in 80 human OSCC tissue samples and in non-cancerous tissue samples. Compared to that in the non-cancerous tissue, the expression of CHK1, GADD45A, and MDM2 in OSCC tissue was significantly increased. The protein expression of the tumour suppressor gene P53 was also increased. Patients with high CHK1 and MDM2 expression levels had a reduced survival time and a poor prognosis, whereas patients with high GADD45A expression levels had a good prognosis. Our results indicate that high CHK1 expression is an independent risk factor for poor OSCC prognosis, and that CHK1 may be a potential target for OSCC clinical treatment.

An Efficient Residual-Based Method for Railway Image Dehazing.

Trains shuttle in semiopen environments, and the surrounding environment plays an important role in the safety of train operation. The weather is one of the factors that affect the surrounding environment of railways. Under haze conditions, railway monitoring and staff vision could be blurred, threatening railway safety. This paper tackles image dehazing for railways. The contributions of this paper for railway video image dehazing are as follows: (1) this paper proposes an end-to-end residual block-based haze removal method that consists of two subnetworks, namely fine-grained and coarse-grained network can directly generate the clean image from input hazy image, called RID-Net (Railway Image Dehazing Network). (2) The combined loss function (per-pixel loss and perceptual loss functions) is proposed to achieve both low-level features and high-level features so to generate the high-quality restored images. (3) We take the full-reference criterion (PSNR&SSIM), object detection, running time, and sensory vision to evaluate the proposed dehazing method. Experimental results on railway synthesized dataset, benchmark indoor dataset, and real-world dataset demonstrate our method has superior performance compared to the state-of-the-art methods.

An Extended FMEA Model Based on Cumulative Prospect Theory and Type-2 Intuitionistic Fuzzy VIKOR for the Railway Train Risk Prioritization.

This paper aims toward the improvement of the limitations of traditional failure mode and effect analysis (FMEA) and examines the crucial failure modes and components for railway train operation. In order to overcome the drawbacks of current FMEA, this paper proposes a novel risk prioritization method based on cumulative prospect theory and type-2 intuitionistic fuzzy VIKOR approach. Type-2 intuitionistic VIKOR handles the combination of the risk factors with their entropy weight. Triangular fuzzy number intuitionistic fuzzy numbers (TFNIFNs) applied as type-2 intuitionistic fuzzy numbers (Type-2 IFNs) are adopted to depict the uncertainty in the risk analysis. Then, cumulative prospect theory is employed to deal with the FMEA team member's risk sensitiveness and decision-making psychological behavior. Finally, a numerical example of the railway train bogie system is selected to illustrate the application and feasibility of the proposed extended FMEA model in this paper, and a comparison study is also performed to validate the practicability and effectiveness of the novel FMEA model. On this basis, this study can provide guidance for the risk prioritization of railway trains and indicate a direction for further research of risk management of rail traffic.

Differences between national and international guidelines for the management of diabetic foot disease.

AIM: No studies have investigated if national guidelines to manage diabetic foot disease differ from international guidelines. This study aimed to compare guidelines of Western Pacific nations with the International Working Group on the Diabetic Foot (IWGDF) guidance documents. METHODS: The 77 recommendations in five chapters of the 2015 IWGDF guidance documents were used as the international gold standard reference. The IWGDF national representative(s) from 12 Western Pacific nations were invited to submit their nation's diabetic foot guideline(s). Four investigators rated information in the national guidelines as "similar," "partially similar," "not similar," or "different" when compared with IWGDF recommendations. National representative(s) reviewed findings. Disagreements in ratings were discussed until consensus agreement achieved. RESULTS: Eight of 12 nations (67%) responded: Australia, China, New Zealand, Taiwan, and Thailand provided national guidelines; Singapore provided the Association of Southeast Asian Nations guidelines; and Hong Kong and the Philippines advised no formal national diabetic foot guidelines existed. The six national guidelines included were 39% similar/partially similar, 58% not similar, and 2% different compared with the IWGDF recommendations. Within individual IWGDF chapters, the six national guidelines were similar/partially similar with 53% of recommendations for the IWGDF prevention chapter, 42% for wound healing, 40% for infection, 40% for peripheral artery disease, and 20% for offloading. CONCLUSIONS: National diabetic foot disease guidelines from a large and diverse region of the world showed limited similarity to recommendations made by international guidelines. Differences between recommendations may contribute to differences in national diabetic foot disease outcomes and burdens.

Rolling Element Bearing Fault Diagnosis under Impulsive Noise Environment Based on Cyclic Correntropy Spectrum.

Rolling element bearings are widely used in various industrial machines. Fault diagnosis of rolling element bearings is a necessary tool to prevent any unexpected accidents and improve industrial efficiency. Although proved to be a powerful method in detecting the resonance band excited by faults, the spectral kurtosis (SK) exposes an obvious weakness in the case of impulsive background noise. To well process the bearing fault signal in the presence of impulsive noise, this paper proposes a fault diagnosis method based on the cyclic correntropy (CCE) function and its spectrum. Furthermore, an important parameter of CCE function, namely kernel size, is analyzed to emphasize its critical influence on the fault diagnosis performance. Finally, comparisons with the SK-based Fast Kurtogram are conducted to highlight the superiority of the proposed method. The experimental results show that the proposed method not only largely suppresses the impulsive noise, but also has a robust self-adaptation ability. The application of the proposed method is validated on a simulated signal and real data, including rolling element bearing data of a train axle.

[Clinical effect of tacrolimus combined with glucocorticoid in the treatment of IgA nephropathy in children].

OBJECTIVE: To study the clinical effect and safety of tacrolimus (TAC) combined with glucocorticoid (GC) versus mycophenolate mofetil (MMF) combined with GC in the treatment of primary IgA nephropathy (IgAN) in children. METHODS: A retrospective analysis was performed for the clinical data of children with primary IgAN confirmed by renal pathology between January 2012 and December 2017. These children were divided into TAC group and MMF group according to the treatment regimen. Their clinical data before treatment and at 1, 3, and 6 months of treatment were collected, and the remission status of IgAN and adverse reactions were compared between the two groups. RESULTS: A total of 43 children who met the inclusion criteria were enrolled, with 15 children in the TAC group and 28 children in the MMF group. At 1 month of treatment, there was no significant difference in the remission status between the two groups (P>0.05). At 3 and 6 months of treatment, the TAC group had a significantly better remission status than the MMF group (P<0.05). At 1 month of treatment, the TAC group had higher serum albumin levels than the MMF group (P<0.05). Both groups had a significant increase in serum albumin levels at each time point after treatment (P<0.0083) and a significant increase in the glomerular filtration rate (GFR) at 3 and 6 months of treatment (P<0.0083). There was no significant difference in the overall incidence rate of adverse reactions between the two groups (P>0.05), but fungal infection was observed in one child from the TAC group. CONCLUSIONS: TAC combined with GC can effectively reduce urinary protein in children with primary IgAN, and it has a better short-term clinical effect than MMF combined with GC, with good safety.

Evaluation of metastatic niches in distant organs after surgical removal of tumor-bearing lymph nodes.

BACKGROUND: Surgical removal of primary tumors can promote the incidence of tumor metastasis. However, molecular mechanisms underlying this process remain unclear. METHODS: We inoculated tumor cells expressing luciferase gene  into subiliac lymph node (SiLN) of the MXH10/Mo-lpr/lpr mice. The tumor-bearing SiLNs were surgically removed at a certain period of time after inoculation. RESULTS: In vivo bioluminescence imaging system and histological staining revealed metastasis in lung, proper axillary lymph node (PALN) and liver. The lung metastasis rate in SiLN removal groups was significantly higher than in the control group using Fisher exact test. Mann-Whitney U-test indicated that the luciferase-positive tumor cells in the lung and liver were significantly higher than in the control groups. The lung samples in SiLN removal groups had strong expression of lysine oxidase (LOX). Moreover, the number of CD11b+ cells in the lung and liver in the SiLN removal groups was significantly increased, which was positively correlated with LOX expression level. In addition, the condition of LOX and CD11b in liver was similar to lung. In the SiLN surgical removal groups, the matrix metalloproteinase (MMP)-2 and VEGFA expression in the lung tissues was significantly higher than in the control groups; the collagen fibers per area around the pulmonary vessels was quite significantly lower and negatively correlated with the expression of MMP-2 by Spearman's analysis. Our data indicated that the reticular fibers were deposited and disordered in the tumor tissues of the lungs in the removal groups, and the reticular fibers per area was higher than in the control groups. The tumor cells in the PALN of control groups were significantly higher than in the SiLN removal groups, and CD169+ and CD11c+ cells were also higher than in the SiLN removal groups. CONCLUSIONS: Altogether, surgical removal of the tumor-bearing lymph node promoted tumor metastasis through changing the niche in lung and liver. Treatment targeting the metastatic niche might be an effective strategy to prevent tumor metastasis, thereby possibly increasing the survival and reducing the incidence of metastasis in cancer patients.

A Semi-Supervised Approach to Bearing Fault Diagnosis under Variable Conditions towards Imbalanced Unlabeled Data.

Fault diagnosis of rolling element bearings is an effective technology to ensure the steadiness of rotating machineries. Most of the existing fault diagnosis algorithms are supervised methods and generally require sufficient labeled data for training. However, the acquisition of labeled samples is often laborious and costly in practice, whereas there are abundant unlabeled samples which also imply health information of bearings. Thus, it is worthwhile to develop semi-supervised methods of fault diagnosis to make effective use of the plentiful unlabeled samples. Nevertheless, considering the normal data are much more than the faulty ones, the problem of imbalanced data exists among unlabeled samples for fault diagnosis. Besides, in practice, bearings often work under uncertain and variable operation conditions, which would also have negative influence on fault diagnosis. To solve these issues, a novel hybrid method for bearing fault diagnosis is proposed in this paper: (1) Inspired by visibility graph, a novel fault feature extraction method named visibility graph feature (VGF) is proposed. The obtained features by VGF are natively insensitive to variable conditions, which has been validated by a simulation experiment in this paper; (2) On basis of VGF, to deal with imbalanced unlabeled data, graph-based rebalance semi-supervised learning (GRSSL) for fault diagnosis is proposed. In GRSSL, a graph based on a weighted sparse adjacency matrix is constructed by the k-nearest neighbors and Gaussian Kernel weighting algorithm by means of the samples. Then, a bivariate cost function over classification and normalized label variable is built up to rebalance the importance of labels. Finally, the proposed VGF-GRSSL method was verified by data collected from Case Western Reserve University Bearing Data Center. The experiment results show that the proposed method of bearing fault diagnosis performs effectively to deal with the imbalanced unlabeled data under variable conditions.

Spectral Kurtosis Entropy and Weighted SaE-ELM for Bogie Fault Diagnosis under Variable Conditions.

Bogies are crucial for the safe operation of rail transit systems and usually work under uncertain and variable operating conditions. However, the diagnosis of bogie faults under variable conditions has barely been discussed until now. Thus, it is valuable to develop effective methods to deal with variable conditions. Besides, considering that the normal data for training are much more than the faulty data in practice, there is another problem in that only a small amount of data is available that includes faults. Concerning these issues, this paper proposes two new algorithms: (1) A novel feature parameter named spectral kurtosis entropy (SKE) is proposed based on the protrugram. The SKE not only avoids the manual post-processing of the protrugram but also has strong robustness to the operating conditions and parameter configurations, which have been validated by a simulation experiment in this paper. In this paper, the SKE, in conjunction with variational mode decomposition (VMD), is employed for feature extraction under variable conditions. (2) A new learning algorithm named weighted self-adaptive evolutionary extreme learning machine (WSaE-ELM) is proposed. WSaE-ELM gives each sample an extra sample weight to rebalance the training data and optimizes these weights along with the parameters of hidden neurons by means of the self-adaptive differential evolution algorithm. Finally, the hybrid method based on VMD, SKE, and WSaE-ELM is verified by using the vibration signals gathered from real bogies with speed variations. It is demonstrated that the proposed method of bogie fault diagnosis outperforms the conventional methods by up to 4.42% and 6.22%, respectively, in percentages of accuracy under variable conditions.

Adaptive Multiclass Mahalanobis Taguchi System for Bearing Fault Diagnosis under Variable Conditions.

Bearings are vital components in industrial machines. Diagnosing the fault of rolling element bearings and ensuring normal operation is essential. However, the faults of rolling element bearings under variable conditions and the adaptive feature selection has rarely been discussed until now. Thus, it is essential to develop a practicable method to put forward the disposal of the fault under variable conditions. Considering these issues, this paper uses the method based on the Mahalanobis Taguchi System (MTS), and overcomes two shortcomings of MTS: (1) MTS is an effective tool to classify faults and has strong robustness to operating conditions, but it can only handle binary classification problems, and this paper constructs the multiclass measurement scale to deal with multi-classification problems. (2) MTS can determine important features, but uses the hard threshold to select the features, and this paper selects the proper feature sequence instead of the threshold to overcome the lesser adaptivity of the threshold configuration for signal-to-noise gain. Hence, this method proposes a novel method named adaptive Multiclass Mahalanobis Taguchi system (aMMTS), in conjunction with variational mode decomposition (VMD) and singular value decomposition (SVD), and is employed to diagnose the faults under the variable conditions. Finally, this method is verified by using the signal data collected from Case Western Reserve University Bearing Data Center. The result shows that it is accurate for bearings fault diagnosis under variable conditions.

Adaptive Diagnosis for Rotating Machineries Using Information Geometrical Kernel-ELM Based on VMD-SVD.

Rotating machineries often work under severe and variable operation conditions, which brings challenges to fault diagnosis. To deal with this challenge, this paper discusses the concept of adaptive diagnosis, which means to diagnose faults under variable operation conditions with self-adaptively and little prior knowledge or human intervention. To this end, a novel algorithm is proposed, information geometrical extreme learning machine with kernel (IG-KELM). From the perspective of information geometry, the structure and Riemannian metric of Kernel-ELM is specified. Based on the geometrical structure, an IG-based conformal transformation is created to improve the generalization ability and self-adaptability of KELM. The proposed IG-KELM, in conjunction with variation mode decomposition (VMD) and singular value decomposition (SVD) is utilized for adaptive diagnosis: (1) VMD, as a new self-adaptive signal processing algorithm is used to decompose the raw signals into several intrinsic mode functions (IMFs). (2) SVD is used to extract the intrinsic characteristics from the matrix constructed with IMFs. (3) IG-KELM is used to diagnose faults under variable conditions self-adaptively with no requirement of prior knowledge or human intervention. Finally, the proposed method was applied on fault diagnosis of a bearing and hydraulic pump. The results show that the proposed method outperforms the conventional method by up to 7.25% and 7.78% respectively, in percentages of accuracy.

Traffic Vehicle Counting in Jam Flow Conditions Using Low-Cost and Energy-Efficient Wireless Magnetic Sensors.

The jam flow condition is one of the main traffic states in traffic flow theory and the most difficult state for sectional traffic information acquisition. Since traffic information acquisition is the basis for the application of an intelligent transportation system, research on traffic vehicle counting methods for the jam flow conditions has been worthwhile. A low-cost and energy-efficient type of multi-function wireless traffic magnetic sensor was designed and developed. Several advantages of the traffic magnetic sensor are that it is suitable for large-scale deployment and time-sustainable detection for traffic information acquisition. Based on the traffic magnetic sensor, a basic vehicle detection algorithm (DWVDA) with less computational complexity was introduced for vehicle counting in low traffic volume conditions. To improve the detection performance in jam flow conditions with a "tailgating effect" between front vehicles and rear vehicles, an improved vehicle detection algorithm (SA-DWVDA) was proposed and applied in field traffic environments. By deploying traffic magnetic sensor nodes in field traffic scenarios, two field experiments were conducted to test and verify the DWVDA and the SA-DWVDA algorithms. The experimental results have shown that both DWVDA and the SA-DWVDA algorithms yield a satisfactory performance in low traffic volume conditions (scenario I) and both of their mean absolute percent errors are less than 1% in this scenario. However, for jam flow conditions with heavy traffic volumes (scenario II), the SA-DWVDA was proven to achieve better results, and the mean absolute percent error of the SA-DWVDA is 2.54% with corresponding results of the DWVDA 7.07%. The results conclude that the proposed SA-DWVDA can implement efficient and accurate vehicle detection in jam flow conditions and can be employed in field traffic environments.