A New Electronic Instrumentation Approach for the Acquisition of Microscopic Blood Smear Images for the Automatic Diagnosis of Anemia

Anemia is a blood abnormality that affects the quantity and quality of red blood cells in the human body. This sometimes banal sign spares no continent and no social stratum. This anomaly is generally appreciated through biological analyzes of patients’ blood. These analyzes, which boil down to the knowledge of hemato-metric constants, cannot by themselves allow the characterization of certain forms of anemia in the sense that most anemia are related to the morphology and color of red blood cells. Our work in this paper is to perform blood smears on patients and perform a morphological and colorimetric analysis of red blood cells on these smears. This approach allowed us to highlight on each erythrocyte morphological and colorimetric descriptors to accurately identify the types of anemia by image processing methods. This identification is performed in an automated environment to allow pathologists to respond quickly to anemia-related emergencies and also improve the treatment to be conducted. This automation required the implementation of a new approach to electronic instrumentation and the acquisition of microscopic blood smear images for the automatic and rapid diagnosis of anemia.

Treatment of Aplastic Anemia by the Kidney-Tonifying and Mediating Method

In this series of 65 cases of aplastic anemia, 26 cases were treated by the kidney-tonifying and mediating method, 19 cases by western drugs, and the remaining 20 cases only by tonifying the kidney as controls. The results showed that the kidney-tonifying and mediating method was significantly superior in the total effective rate to the method of western drugs and that of tonifying the kidney alone (P

Method for Analog Circuit Soft-Fault Diagnosis and Parameter Identification Based on Indictor of Phase Deviation and Spectral Radius

The soft fault induced by parameter variation is one of the most challenging problems in the domain of fault diagnosis for analog circuits.A new fault location and parameter prediction approach for soft-faults diagnosis in analog circuits is presented in this paper.The proposed method extracts the original signals from the output terminals of the circuits under test（CUT） by a data acquisition board.Firstly,the phase deviation value between fault-free and faulty conditions is obtained by fitting the sampling sequence with a sine curve.Secondly,the sampling sequence is organized into a square matrix and the spectral radius of this matrix is obtained.Thirdly,the smallest error of the spectral radius and the corresponding component value are obtained through comparing the spectral radius and phase deviation value with the trend curves of them,respectively,which are calculated from the simulation data.Finally,the fault location is completed by using the smallest error,and the corresponding component value is the parameter identification result.Both simulated and experimental results show the effectiveness of the proposed approach.It is particularly suitable for the fault location and parameter identification for analog integrated circuits.

A Novel Parsimonious Neurofuzzy Model Applied to Railway Carriage System Identification and Fault Diagnosis

In this paper, we suggest a novel parsimonious neurofuzzy model realized by RBFNs for railway carriage system identification and fault diagnosis. To overcome the curse of dimensionality resulting from high dimensional input variables, in our developed model the features extracted from the available observations are regarded as the input variables by adopting the higher-order statistics(HOS) technique. Such a constructed model is also applied to a practical railway carriage system, simulation results indicate that the developed neurofuzzy model possesses strong identification and fault diagnosis ability.

Clinical diagnosis,treatment,and medical identification of specific pulmonary infection in naval pilots:Four case reports

BACKGROUND Specific pulmonary infection could seriously threaten the health of pilots and their companions.The consequences are serious.We investigated the clinical diagnosis,treatment,and medical identification of specific pulmonary infections in naval pilots.CASE SUMMARY We analyzed the medical waiver and clinical data of four pilots with specific pulmonary infections,who had accepted treatment at the Naval Medical Center of Chinese People’s Liberation Army between January 2020 and November 2021,including three cases of tuberculosis and one of cryptococcal pneumonia.All cases underwent a series of comprehensive treatment courses.Three cases successfully obtained medical waiver for flight after being cured,while one was grounded after reaching the maximum flight life after being cured.CONCLUSION Chest computed tomography scanning should be used instead of chest radiography in pilots’physical examination.Most pilots with specific pulmonary infection can be cured and return to flight.

Ellipsoidal bounding set-membership identification approach for robust fault diagnosis with application to mobile robots

A robust fault diagnosis approach is developed by incorporating a set-membership identification (SMI) method. A class of systems with linear models in the form of fault related parameters is investigated, with model uncertainties and parameter variations taken into account explicitly and treated as bounded errors. An ellipsoid bounding set-membership identification algorithm is proposed to propagate bounded uncertainties rigorously and the guaranteed feasible set of faults parameters enveloping true parameter values is given. Faults arised from abrupt parameter variations can be detected and isolated on-line by consistency check between predicted and observed parameter sets obtained in the identification procedure. The proposed approach provides the improved robustness with its ability to distinguish real faults from model uncertainties, which comes with the inherent guaranteed robustness of the set-membership framework. Efforts are also made in this work to balance between conservativeness and computation complexity of the overall algorithm. Simulation results for the mobile robot with several slipping faults scenarios demonstrate the correctness of the proposed approach for faults detection and isolation (FDI).

Fault Diagnosis Based on Fuzzy Support Vector Machine with Parameter Tuning and Feature Selection

This study describes a classification methodology based on support vector machines(SVMs),which offer superior classification performance for fault diagnosis in chemical process engineering.The method incorporates an efficient parameter tuning procedure(based on minimization of radius/margin bound for SVM's leave-one-out errors)into a multi-class classification strategy using a fuzzy decision factor,which is named fuzzy support vector machine(FSVM).The datasets generated from the Tennessee Eastman process(TEP)simulator were used to evaluate the clas-sification performance.To decrease the negative influence of the auto-correlated and irrelevant variables,a key vari-able identification procedure using recursive feature elimination,based on the SVM is implemented,with time lags incorporated,before every classifier is trained,and the number of relatively important variables to every classifier is basically determined by 10-fold cross-validation.Performance comparisons are implemented among several kinds of multi-class decision machines,by which the effectiveness of the proposed approach is proved.

Research and Application of Image Recognition Technology in Microscopy Diagnosis of Catalytic Cracking Catalysts

The image processing and recognition technology is used in the micrographic diagnosis of the FCC catalyst. By quickly analyzing the image and quickly obtaining the analysis result of the catalyst property, the accident of the FCC unit may be alerted. In this paper, the image recognition technology is used to better diagnose the catalytic cracking catalyst.

Wear Fault Diagnosis of Machinery Based on Neural Networks and Gray Relationships

In this paper, the regular characteristic of -wear particles related to fault type of machines based on condition monitoring of reciprocal machinery is discussed. The typical -wear particles spectrum is established according to the equipment structure , friction and wear rule and the characteristic of 'wear particles; The identification technology of wear particles is proposed based on neural networks and a gray relationship ; an intelligent wear particles identification system is designed. The diagnosis example shows that this system can promote the accuracy and the speed of wear particles identification.

Clinical applications of high-throughput genetic diagnosis in inherited retinal dystrophies: Present challenges and future directions

The advent of next generation sequencing(NGS) tech-niques has greatly simplified the molecular diagnosis and gene identification in very rare and highly heterogeneous Mendelian disorders. Over the last two years, these approaches, especially whole exome sequencing(WES), alone or combined with homozygosity mapping and linkage analysis, have proved to be successful in the identification of more than 25 new causative retinal dystrophy genes. NGS-approaches have also identified a wealth of new mutations in previously reported genes and have provided more comprehensive information concerning the landscape of genotype-phenotype correlations and the genetic complexity/diversity of human control populations. Although whole genome sequencing is far more informative than WES, the functional meaning of the genetic variants identified by the latter can be more easily interpreted, and final diagnosis of inherited retinal dystrophies is extremely successful, reaching 80%, particularly for recessive cases. Even considering the present limitations of WES, the reductions in costs and time, the continual technical improvements, the implementation of refined bioinformatic tools and the unbiased comprehensive genetic information it provides, make WES a very promising diagnostic tool for routine clinical and genetic diagnosis in the future.

Cation-exchange high-performance liquid chromatography for variant hemoglobins and HbF/A2:What must hematopathologists know about methodology?

Cation-exchange high-performance liquid chromatography(CE-HPLC) is a widely used laboratory test to detect variant hemoglobins as well as quantify hemoglobins F and A2 for the diagnosis of thalassemia syndromes. It's versatility, speed, reproducibility and convenience have made CE-HPLC the method of choice to initially screen for hemoglobin disorders. Despite its popularity, several methodological aspects of the technology remain obscure to pathologists and this may have consequences in specific situations. This paper discusses the basic principles of the technique, the initial quality control steps and the interpretation of various controls and variables that are available on the instrument output. Subsequent sections are devoted to methodological considerations that arise during reporting of cases. For instance, common problems of misidentified peaks, totals crossing 100%, causes of total area being above or below acceptable limits and the importance of pre-integration region peaks are dealt with. Ultimately, CE-HPLC remains an investigation, the reporting of which combines in-depth knowledge of the biological basics with more than a working knowledge of the technological aspects of the technique.

A model for automatic identification of human pulse signals

This paper presents a quantitative method for automatic identification of human pulse signals. The idea is to start with the extraction of characteristic parameters and then to construct the recognition model based on Bayesian networks. To identify depth, frequency and rhythm, several parameters are proposed. To distinguish the strength and shape, which cannot be represented by one or several parameters and are hard to recognize, the main time-domain feature parameters are computed based on the feature points of the pulse signal. Then the extracted parameters are taken as the input and five models for automatic pulse signal identification are constructed based on Bayesian networks. Experimental results demonstrate that the method is feasible and effective in recognizing depth, frequency, rhythm, strength and shape of pulse signals, which can be expected to facilitate the modernization of pulse diagnosis.

Loop-Mediated Isothermal Amplification (LAMP) for the Detection of <i>Listeria monocytogenes</i>and Major Pathogenic Serotypes

Rapid identification and characterization of Listeria monocytogenes are required for the food industry, epidemiological studies, and disease prevention and control. However, typing procedures are labor-intensive and time-consuming, and they require technical expertise, a panel of sera and reference culture strains or sophisticated and expensive equipment. To improve upon traditional diagnostic methods for L. monocytogenes we developed and evaluated an efficient procedure for the specific identification of L. monocytogenes and the major pathogenic serotypes of the species based on loop-mediated isothermal amplification (LAMP). Four individual reactions were designed using primers targeting any L. monocytogenes serotypes (LAMP-AS) and the 1/2a (LAMP-1/2a), 1/2b (LAMP-1/2b), and 4b (LAMP-4b) serotypes. The procedure distinguished L. monocytogenes from closely genetically related species and the targeted serotypes. Cross-reactivity with a few rare serotypes isolated from food or clinical samples did not impair the usefulness of the procedure. Thus, our approach constitutes a fast, easy and low-cost alternative for L. monocytogenes diagnosis and serotyping and may be useful for surveillance and epidemiological investigation programs.

Classification Methods Based on Pattern Discrimination Models for Web-Based Diagnosis of Rice Diseases

Two classification and identification methods based on pattern discrimination models and the majority-vote technique were investigated for implementing a World Wide Web-based system for the identification of rice diseases. The experiment was carried out using color and shape patterns in 425 images of three rice diseases, which were classified into four classes： two classes of leaf blast, and one class each of sheath blight and brown spot. A method consisting of two discrimination steps involving application of multiple discrimination models of a support vector machine gave the best result because of its capacity to evaluate the similarity of disease types. This accuracy of the method was 88% for leaf blast （A-type）, 94% for sheath blight, and 80% for leaf blast （B-type） and brown spot; on average, the accuracy of this method was 5% greater than that of the other method when three classes were used in the model. Although the accuracy of both methods was inadequate, the results of this study show that it is possible to estimate the least number of possible or similar diseases from a large number of diseases. Therefore, we conclude that there is merit in grouping classes into subgroups rather than attempting to discriminate between all classes simultaneously and that these methods are effective in identifying diseases for web-based diagnosis.

Integrated Generative Adversarial Network and XGBoost for Anomaly Processing of Massive Data Flow in Dispatch Automation Systems

Existing power anomaly detection is mainly based on a pattern matching algorithm.However,this method requires a lot of manual work,is time-consuming,and cannot detect unknown anomalies.Moreover,a large amount of labeled anomaly data is required in machine learning-based anomaly detection.Therefore,this paper proposes the application of a generative adversarial network(GAN)to massive data stream anomaly identification,diagnosis,and prediction in power dispatching automation systems.Firstly,to address the problem of the small amount of anomaly data,a GAN is used to obtain reliable labeled datasets for fault diagnosis model training based on a few labeled data points.Then,a two-step detection process is designed for the characteristics of grid anomalies,where the generated samples are first input to the XGBoost recognition system to identify the large class of anomalies in the first step.Thereafter,the data processed in the first step are input to the joint model of Convolutional Neural Networks(CNN)and Long short-term memory(LSTM)for fine-grained analysis to detect the small class of anomalies in the second step.Extensive experiments show that our work can reduce a lot of manual work and outperform the state-of-art anomalies classification algorithms for power dispatching data network.

原发性干燥综合征合并血细胞减少中医诊治研究进展

原发性干燥综合征(pSS)是一种慢性炎症性自身免疫病,除累及外分泌腺之外,还可累及多器官、系统,其中以血液系统受累最为常见,多表现为一系或多系血细胞减少。目前对于pSS合并血细胞减少缺乏统一公认的治疗方案,西医治疗多以糖皮质激素、免疫抑制剂为主,而中医药治疗本病具有较好疗效。通过总结pSS合并血细胞减少的中医诊治经验,并对相关研究进行综述后发现,现代医家认为pSS核心病机为气阴亏虚,久之则产生热、毒、瘀等多种病理产物,相互影响,导致全身气血津液失常,引起血细胞减少。本病病位多责之于肝、脾、肾,治以滋阴、益气、养血之法,辅以清热解毒、化瘀通络之法,并根据其合并血细胞减少类型的不同以及是否使用糖皮质激素用药各有侧重。中医药治疗本病较西药治疗能够更好地改善血细胞水平且不良事件发生率更低,具有一定优势。

IGIgram:An Improved Gini Index-Based Envelope Analysis for Rolling Bearing Fault Diagnosis

The transient impulse features caused by rolling bearing faults are often present in the resonance frequency band which is closely related to the dynamic characteristics of the machine structure.Informative frequency band identification is a crucial prerequisite for envelope analysis and thereby accurate fault diagnosis of rolling bearings.In this paper,based on the ratio of quasi-arithmetic means and Gini index,improved Gini indices(IGIs)are proposed to quantify the transient impulse features of a signal,and their effectiveness and advantages in sparse quantification are confirmed by simulation analysis and comparisons with traditional sparsity measures.Furthermore,an IGI-based envelope analysis method named IGIgram is developed for fault diagnosis of rolling bearings.In the new method,an IGI-based indicator is constructed to evaluate the impulsiveness and cyclostationarity of the narrow-band filtered signal simultaneously,and then a frequency band with abundant fault information is adaptively determined for extracting bearing fault features.The performance of the IGIgram method is verified on the simulation signal and railway bearing experimental signals and compared with typical sparsity measures-based envelope analysis methods and log-cycligram.The results demonstrate that the proposed IGIs are efficient in quantifying bearing fault-induced transient features and the IGIgram method with appropriate power exponent can effectively achieve the diagnostics of different axle-box bearing faults.

Novel Fault Diagnosis Scheme for HVDC System via ESO

A novel fault detection and identification(FDI)scheme for HVDC(High Voltage Direct Current Transmission)system was presented.It was based on the unique active disturbance rejection concept,where the HVDC system faults were estimated using an extended states observer(ESO).Firstly,the mathematical model of HVDC system was constructed,where the system states and disturbance were treated as an extended state.An augment HVDC system was established by using the extended state in rectify side and converter side,respectively.Then,a fault diagnosis filter was established to diagnose the HVDC system faults via the ESO theory.The evolution of the extended state in the augment HVDC system can reflect the actual system faults and disturbances,which can be used for the fault diagnosis purpose.A novel feature of this approach is that it can simultaneously detect and identify the shape and magnitude of the HVDC faults and disturbance.Finally,different kinds of HVDC faults were simulated to illustrate the feasibility and effectiveness of the proposed ESO based FDI approach.Compared with the neural network based or support vector machine based FDI approach,the ESO based FDI scheme can reduce the fault detection time dramatically and track the actual system fault accurately.What's more important,it needs not do complex online calculations and the training of neural network so that it can be applied into practice.

基于QAR数据的飞行控制系统故障研究综述

为系统梳理国内外对民用飞机飞行控制系统故障分析的研究历程和现状,针对基于快速存取记录器(QAR)数据分析的飞行控制系统典型故障类型,首先,总结QAR数据预处理、特征提取等使用过程;然后,根据故障分析可达到的性能指标,提出4个故障研究阶段,分别为故障监测、故障识别、故障诊断和故障预测;最后,综合国内外研究进度与深度,得出飞行控制系统典型故障类型,包括方向舵液压泄漏、升降舵指示不一致、襟翼动作耗时等,建模常用QAR数据项包括飞机主舵面位置、飞行姿态、飞机性能、左右襟翼角度、襟翼位置等,计算方法包括物理模型、多变量统计、逻辑推理、机器学习等。结果表明:系统分析方向舵、升降舵、襟翼等子系统最新研究进展,发现在故障类型、参数选择和计算方法的改进等方面取得了一定的成果,故障研究阶段基本处于故障诊断或非实时预测水平,但仍需加强面向安全保障与实际维修方面的需求,以实现故障实时预测技术。

基于识别规则的注水站离心泵故障预测方法

针对油田注水站通过传统的人工“望闻问切”等手段,分析判断离心泵机组工况现状的不足,提出了一种基于识别规则的智能故障诊断方法,实现了离心泵实时故障诊断。首先,通过采集注水站离心泵正常状态以及异常状态时的参数,接着分析两种状态下这些参数的变化趋势,将其进行对比分析,研究出离心泵在正常状态和异常状态下参数变化的特征。再次,通过这些离心泵机组参数的变化率及其阈值,计算出离心泵机组故障异常的边界条件。最后,研制出一种离心泵故障识别的规则,实现对离心泵故障的智能预测。通过油田离心泵机组的现场验证,该方法预测故障的准确率达到了99.5%,确保了离心泵机组正常生产运行。