Railway wheel fault diagnosis using a fuzzy-logic method

A simple method for diagnosis of railway wheel defects using fuzzy-logic is proposed. The method is based on vibration measurements at different train speeds on healthy wheels and wheels with defects known a priori. The measured vibrations are statistically analyzed and confidence intervals for healthy and defective wheels depending on train speed and frequency of analysis are established. To facilitate the implementation of the method a fuzzy-logic is adopted. The fuzzy-logic model stores the obtained experience in a database and performs the decision making on damage extent and consequently the need of preventive maintenance. In view of the obtained results, the advantages and the limitations of the proposed approach as well as suggestions for further improvements are presented and discussed.     

Progress on the application of electrochemiluminescence biosensor based on nanomaterials

In this review, we check out the number of published literature in the field of ECL biosensors trends during nearly a decade, and compare the research status of four different types of biosensors; summarize the application forms of nanomaterials in ECL biosensor. We have an overview of the building patterns and application example of the four main types of biosensors in the paper.     

微量元素锌在治疗Bell麻痹中的作用

为探讨急性早期应用补锌和综合疗法治疗Bell麻痹 ,将 1 5 8例Bell麻痹患者 ,随机分成两组 :(1 )补锌组 (补锌 +综合疗法 ) ;(2 )对照组 (单纯综合疗法 )。对其进行了观察对比治疗 ,同时检测了所有患者治疗前后自身对照发锌值 ,还对所有患者进行了定位诊断、面神经电反应测试、并对面肌功能进行评分。结果表明 ,对定位诊断其病变在D段、面肌功能评分在 1 1分以下、面神经无电反应患者 ,即使给予早期综合治疗 ,效果仍较差 ,而早期给予补锌加综合治疗效果优于对照组 ,其它段内患者治疗次数和治愈天数也均优于对照组。可见微量元素锌在发挥免疫应答、免疫调控中的重要作用     

Bearing Fault Diagnosis Method Based on Convolutional Neural Network and Knowledge Graph

An effective fault diagnosis method of bearing is the key to predictive maintenance of modern industrial equipment. With the single use of equipment failure mechanism or operation of data, it is hard to resolve multiple complex variable working conditions, multiple types of fault and equipment malfunctions and failures related to knowledge and data. In order to solve these problems, a fault diagnosis method based on the fusion of deep learning with a knowledge graph is proposed in this paper. Firstly, the knowledge rules of bearing data is used for entity extraction. Next, the multiscale optimized convolutional neural network (MOCNN) proposed in this paper is used for fault classification to achieve relationship extraction. Finally, the fault diagnosis graph of the bearing is constructed for fault-assisted decision-making as well as the detailed display of fault information. According to experiment analysis, the fault diagnosis model based on MOCNN proposed in this paper, which integrates the end-to-end convolutional neural network and the attention mechanism, still achieves an accuracy of 97.86% under the data set of 160 types of faults. Compared with the deep learning models such as Resnet and Inception in the noise environment of multiple working conditions and variable working conditions, the model proposed in this paper not only shows a faster convergence speed and stable performance, but also a higher accuracy in evaluation indicators, which is beneficial to practical use.     

DAD-Net: Classification of Alzheimer’s Disease Using ADASYN Oversampling Technique and Optimized Neural Network

Alzheimer’s Disease (AD) is a neurological brain disorder that causes dementia and neurological dysfunction, affecting memory, behavior, and cognition. Deep Learning (DL), a kind of Artificial Intelligence (AI), has paved the way for new AD detection and automation methods. The DL model’s prediction accuracy depends on the dataset’s size. The DL models lose their accuracy when the dataset has an imbalanced class problem. This study aims to use the deep Convolutional Neural Network (CNN) to develop a reliable and efficient method for identifying Alzheimer’s disease using MRI. In this study, we offer a new CNN architecture for diagnosing Alzheimer’s disease with a modest number of parameters, making it perfect for training a smaller dataset. This proposed model correctly separates the early stages of Alzheimer’s disease and displays class activation patterns on the brain as a heat map. The proposed Detection of Alzheimer’s Disease Network (DAD-Net) is developed from scratch to correctly classify the phases of Alzheimer’s disease while reducing parameters and computation costs. The Kaggle MRI image dataset has a severe problem with class imbalance. Therefore, we used a synthetic oversampling technique to distribute the image throughout the classes and avoid the problem. Precision, recall, F1-score, Area Under the Curve (AUC), and loss are all used to compare the proposed DAD-Net against DEMENET and CNN Model. For accuracy, AUC, F1-score, precision, and recall, the DAD-Net achieved the following values for evaluation metrics: 99.22%, 99.91%, 99.19%, 99.30%, and 99.14%, respectively. The presented DAD-Net outperforms other state-of-the-art models in all evaluation metrics, according to the simulation results.     

Intelligent Fault Diagnosis of Industrial Robot Based on Multiclass Mahalanobis-Taguchi System for Imbalanced Data

One of the biggest challenges for the fault diagnosis research of industrial robots is that the normal data is far more than the fault data; that is, the data is imbalanced. The traditional diagnosis approaches of industrial robots are more biased toward the majority categories, which makes the diagnosis accuracy of the minority categories decrease. To solve the imbalanced problem, the traditional algorithm is improved by using cost-sensitive learning, single-class learning and other approaches. However, these algorithms also have a series of problems. For instance, it is difficult to estimate the true misclassification cost, overfitting, and long computation time. Therefore, a fault diagnosis approach for industrial robots, based on the Multiclass Mahalanobis-Taguchi system (MMTS), is proposed in this article. It can be classified the categories by measuring the deviation degree from the sample to the reference space, which is more suitable for classifying imbalanced data. The accuracy, G-mean and F-measure are used to verify the effectiveness of the proposed approach on an industrial robot platform. The experimental results show that the proposed approach’s accuracy, F-measure and G-mean improves by an average of 20.74%, 12.85% and 21.68%, compared with the other five traditional approaches when the imbalance ratio is 9. With the increase in the imbalance ratio, the proposed approach has better stability than the traditional algorithms.     

Experiences and Perspectives of GC-MS Application for the Search of Low Molecular Weight Discriminants of Schizophrenia

Schizophrenia is one of the most severe chronic mental disorders that is currently diagnosed and categorized through subjective clinical assessment of complex symptoms. At present, there is a recognized need for an objective, unbiased clinical test for schizophrenia diagnosis at an early stage and categorization of the disease. This can be achieved by assaying low-molecular-weight biomarkers of the disease. Here we give an overview of previously conducted research on the discovery of biomarkers of schizophrenia and focus on the studies implemented with the use of GC-MS and the least invasiveness of biological samples acquisition. The presented data demonstrate that GC-MS is a powerful instrumental platform for investigating dysregulated biochemical pathways implicated in schizophrenia pathogenesis. With this platform, different research groups suggested a number of low molecular weight biomarkers of schizophrenia. However, we recognize an inconsistency between the biomarkers or biomarkers patterns revealed by different groups even in the same matrix. Moreover, despite the importance of the problem, the number of relevant studies is limited. The intensification of the research, as well as the harmonization of the analytical procedures to overcome the observed inconsistencies, can be indicated as future directions in the schizophrenia bio-markers quest.     

探头对共聚焦内窥成像系统层析能力的影响

根据透镜成像规律和单模光纤的传输特性,获得基于单模光纤的共聚焦内窥成像系统轴向光强分布,发现系统的成像过程由于单模光纤的介入呈现非线性,照明光路系统和接收光路系统仅影响系统接收信号的总强度,系统层析能力取决于探头和单模光纤性能.通过建立的共聚焦内窥成像实验装置,测量出在三组不同物镜组合情况下系统的归一化轴向光强分布.结果显示已建立的共聚焦内窥成像系统的理论模型是正确的,扫描光点的定位精度对系统层析能力影响很大,系统设计应该综合考虑光学、机械和电子方面的性能要求.     

A Small-Molecule Diketopyrrolopyrrole-Based Dye for in vivo NIR-IIa Fluorescence Bioimaging

Organic small-molecule fluorophores with near-infrared IIa (NIR-IIa) emission have great potential in pre-clinical detection and inoperative imaging due to the high-spatial resolution and deep penetration. However, developments of the NIR-IIa fluorophores are still facing considerable challenges. In this work, a series of diketopyrrolopyrrole (DPP)-based fluorophores were designed and synthesized. Subsequently, nanomaterial T25@F127 with significant NIR-IIa emission properties was rationally prepared by encapsulating DPP-based fluorophore T25 , and was selected for fluorescence angiography and cerebral vascular microscopic imaging with nearly 800 μm penetrating depth and excellent signal-background ratio of 4.07 and 2.26 (at 250 and 400 μm), respectively. Furthermore, the nanomaterial T25@cRGD with tumor targeting ability can image tiny metastatic tumor on intestine with a small size of 0.3 mm×1.0 mm and high-spatial resolution (SBR=3.84). This study demonstrates that the nanomaterials which encapsulated T25 behave as excellent NIR-IIa fluorescence imaging agents and have a great potential for in vivo biological application.     

Polyoxometalate-Based Nanomaterials Toward Efficient Cancer Diagnosis and Therapy

As an emerging class of inorganic metal oxides, organically functionalized polyoxometalates (POMs) or POM-based nanohybrids have been demonstrated promising potential for the inhibition of various cancer types by the virtue of their diversity in structures and significantly reduced toxicity. This contribution summarizes the latest achievement of POM-based nanomaterials in cancer diagnosis and various therapeutics to put forward our fundamental viewpoints on the design principles of modified POMs based on their application. In addition, major challenges and perspectives in this field are also discussed. We expect that this review will provide a valuable and systematic reference for the further development of POM-based nanomaterials.