Diagnosis Rules Research Articles

Health status diagnosis of distribution transformers based on big data mining

AbstractIn the fault detection technology of distribution transformer, the traditional artificial intelligence algorithm has been unable to achieve its efficient analysis and processing, but also unable to achieve the elimination of misdiagnosis caused by improper interval segmentation in the process of fault diagnosis of distribution transformer. In a word, the problem that always exists in transformer fault diagnosis technology is the problem of discretization of fault data. In order to solve this problem, this paper creatively proposes a health condition diagnosis method of distribution transformer based on large data. This method innovatively proposes that the dissolved gas analysis value of distribution transformer is the conditional attribute, and the fault type is the decision attribute, and the fault decision table is established. The continuous attribute data in the decision table are discretized by using the optimization behavior of large data sets. Subsequently, the discretized decision table is simplified by using the big data theory, and the decision table of fault diagnosis rules is established, which greatly simplifies the difficulty of attribute simplification of decision table and makes diagnosis more convenient. Finally, an example shows that the proposed method can effectively discretize and reduce samples. Compared with traditional methods, it improves the accuracy of fault diagnosis.

MEDICAL STATISTICS: ARE WE RIGHT IN EVALUATING THE MORTALITY RATES OF ONCOLOGIC DISEASES?

The issues of medical statistics on mortality from cancer, especially the correctness of such indicators, attract general interest. In a comparative aspect, mortality rates in different countries are given and the possible influence of various factors on the distortion of these indicators is described. The death rates in the Russian Federation, the Republic of Bashkortostan and Ufa are considered in detail. Comparison of the number of doctors and oncologists in the republics of Bashkortostan and Dagestan revealed some differences in the territories by the availability of doctors, especially oncologists. At the same time, the ratio of morbidity and mortality in these territories are in similar and correct parameters. A detailed analysis of the letters of the Ministry of Health of Russia with recommendations relating to the principles of coding for cancer in combination with another pathology. The statistical incident was the change in the coding rules for a post-mortem diagnosis in 2011 and the peculiarities of the translation of the text from ICD-10. According to the results of the analysis, proposals were developed aimed at the objectification of mortality rates and 5-year survival of cancer patients.

English

The present study develops the Clifford algebra Cl5,0 within a dermatological task to diagnose skin melanoma using images of skin lesions, which are modeled here by means of 5D lesion feature vectors (LFVs). The LFV is a numerical approximation of the most used clinical rule for melanoma diagnosis — ABCD. To generate the Cl5,0 we develop a new formula that uses the entries of a 5D vector to calculate the entries of a 32D multivector. This vector provides a natural mapping of the original 5D vector onto the 2-, 3-, 4-vector Cl5,0 subspaces. We use a sample set of 112 5D LFVs and apply the new formula to calculate 112 32D multivectors in the Cl5,0. Next we map the 5D LFVs onto the 2-, 3-, 4-vector subspaces of the Cl5,0. In every subspace we apply a binary support vector machine to classify the mapped 112 LFVs. With the obtained results we calculate six metrics and evaluate the effectiveness of the diagnosis in every subspace. At the end of the paper we compare the classification results, obtained in every subspace, with the results obtained by the four diagnosing rules most used in clinical practice and contemporary machine learning methods. This way we reveal the potential of using Clifford algebras in the analysis and classification of medical images.

Red flag rules for knee and lower leg differential diagnosis.

Physical therapists can be frequently a patient's initial encounter after a lower extremity injury, as access to physical therapy services have become readily available without a physician's referral. In 2017, over 65% of physical therapy providers are treating via direct access in United States that allow unrestricted access. In addition to direct access, in an age when at least one of four Americans have multiple chronic medical conditions, it is vital to be able to perform a comprehensive examination, which includes a thorough patient history, systems review, and objective test and measures. Physical therapists should identify red flag symptoms and signs indicating possible pathological condition(s). Based on the findings, a clinical decision should be made to either treat the patient, refer the patient to an appropriate healthcare practitioner, or initiate both treatment and referral. If serious pathology is suspected, it is most prudent to refer the patient appropriately to a qualified medical practitioner.

Predictive rules for optical diagnosis of < 10-mm colorectal polyps based on a dedicated software.

The BASIC classification for predicting in vivo colorectal polyp histology incorporates both surface and pit/vessel descriptor domains. This study aimed to define new BASIC classes for adenomatous and hyperplastic polyps. A video library (102 still images/videos of < 10-mm polyps using white-light [WLI] and blue-light imaging [BLI]) was reviewed by seven expert endoscopists. Polyps were rated according to the individual descriptors of the three BASIC domains (surface/pit/vessel). A model to predict polyp histology (adenomatous or hyperplastic) was developed using multivariable logistic regression and subsequent "leave-one-out" cross-validation. New BASIC rules were then defined by Delphi agreement. The overall accuracy of these rules when used by experts was evaluated according to the level of confidence and light type. The strength of prediction for adenomatous histology from 2175 observations assessed by area under the curve (AUC; 95 % confidence interval) was poor-to-fair for the surface descriptors (0.50 [0.33 - 0.69] for mucus; 0.68 [0.57 - 0.79] for irregular surface), but stronger for pits (0.87 [0.80 - 0.96] for featureless/round/not round) and vessels (0.80 [0.65 - 0.87] for not present/lacy/pericryptal). By combining the domains, a good-to-excellent prediction was shown (AUC 0.89 [0.81 - 0.96]). After the definition of new BASIC rules for adenomatous and hyperplastic polyps, accuracy for high confidence BLI predictions was 90.3 % (86.3 % - 93.2 %), which was superior to high confidence WLI (83.7 % [77.3 % - 87.7 %]) and low confidence BLI predictions (77.7 % [61.1 % - 88.6 %]). Based on the strength of prediction, the new BASIC classes for adenomatous and hyperplastic histology show favorable results for accuracy and confidence levels.

A novel layered fuzzy Petri nets modelling and reasoning method for process equipment failure risk assessment

The continuous and stable operations of equipment are the crucial factor for the safety of process industries. Risk assessment has demonstrated its capabilities as a practical method to analyze and prevent process equipment failure. Since the status of equipment is mainly determined by its components and the coupling relationship is complex, a systematical and practicable method is needed to help risk assessment. Petri nets provide a graphical and mathematical representation for risk modelling and reasoning. With the complexity of equipment, there could be lots of diagnosis rules, which makes the network complicated and huge. In order to describe the coupling relationship clearly and make the computational process flexible, a layered Petri nets method is presented in this context to conduct complex rule-based risk analysis and assessment. Moreover, The fuzzy logic is introduced to represent expert knowledge in a semi-quantitative way. The method presented was applied for assessing the risk of reciprocating compressor failure, which is the supercharging equipment widely used in the natural gas transportation process.

Urine lipoarabinomannan (LAM) and antimicrobial usage in seriously-ill HIV-infected patients with sputum smear-negative pulmonary tuberculosis.

Based on current WHO guidelines, hospitalized tuberculosis (TB) and HIV co-infected patients with CD4 count <100 cells/mm3 who are urine lipoarabinomannan (LAM) positive should be initiated on TB treatment. This recommendation is conditional, and data are limited in sputum smear-negative patients from TB endemic countries where the LAM test is largely inaccessible. Other potential benefits of LAM, including reduction in antibiotic usage have, hitherto, not been explored. We consecutively enrolled newly-admitted seriously-ill HIV-infected patients (n=187) with suspected TB from three hospitals in KwaZulu-Natal, South Africa. All patients were empirically treated for TB as per the WHO 2007 smear-negative TB algorithm (patients untreated for TB were not recruited). Bio-banked urine, donated prior to anti-TB treatment, was tested for TB-infection using a commercially available LAM-ELISA test. TB sputum and blood cultures were performed. Data from 156 patients containing CD4 count, urine-LAM, sputum and blood culture results were analysed. Mean age was 37 years, median CD4-count was 75 cells/mm3 [interquartile range (IQR), 34-169 cells/mm3], 54/156 (34.6%) were sputum culture-positive, 12/54 (22.2%) blood-culture positive, and 53/156 (34.0%) LAM-positive. Thus, LAM sensitivity was 55.6% (30/54). The study design did not allow for calculation of specificity. Urine-LAM positivity was associated with low CD4 count (P=0.002). Ninety-point-six percent (48/53) of LAM-positive patients received antibiotics [15/48 (31.3%), 23/48 (47.9%) and 10/48 (20.8%) received one, two or three different antibiotics respectively], while the duration of antibiotic therapy was more than 5 days in 26 of 46 (56.5%) patients. Urine LAM testing in sputum smear-negative severely-ill hospitalized patients with TB-HIV co-infection and advanced immunosuppression, offered an immediate rule-in diagnosis in one-third of empirically treated patients. Moreover, LAM, by providing a rapid alternative diagnosis, could potentially reduce antibiotic overusage in such patients thereby reducing health-care costs and facilitating antibiotic stewardship.

From Deep Learning Towards Finding Skin Lesion Biomarkers.

Melanoma is a type of skin cancer with the most rapidly increasing incidence. Early detection of melanoma using dermoscopy images significantly increases patients' survival rate. However, accurately classifying skin lesions by eye, especially in the early stage of melanoma, is extremely challenging for the dermatologists. Hence, the discovery of reliable biomarkers will be meaningful for melanoma diagnosis. In recent years, the value of deep learning empowered computer-assisted diagnose has been shown in biomedical imaging-based decision making. However, much deep learning research focuses on improving disease detection accuracy but not understanding the features deep learning use to determine the evidence of pathology. We aim to make sure the features used by deep learning methods are the reasonable clinical features for skin lesions diagnosis, rather than artifacts. Further, we aim to discover new biomarkers, which may not have been included in clinical criteria but do make sense to the dermatologists. Our proposed pipeline can find biomarkers for identifying different lesions. The patterns are agreed with dermatologists. Surprisingly, we find surround skins also can be used as evidence for skin lesion diagnosis, which has not been included in traditional diagnosis rules. The biomarkers discovered from deep learning classifier can be significant and useful to guide clinical diagnosis.

Study of joint temporal-spatial distribution of array output for large-scale photovoltaic plant and its fault diagnosis application

The temporal-spatial distribution of photovoltaic (PV) array output in large-scale PV plant has following features: (i) In the DC (direct current) side of PV plant, the outputs from different arrays display a substantial correlation with each other. (ii) The dynamic difference for a PV array is covered by the random fluctuation of PV output. (iii) It is difficult to explain the occurrence and evolution process of PV faults with operation data only. Currently, most fault diagnosis methods for the PV arrays do not take advantage of the temporal-spatial distribution information contained in the operation data. To solve these above problem, a new fault diagnosis method using the spatio-temporal distribution characteristics of photovoltaic array output is proposed. Here, the temporal fluctuation and spatial distribution characteristics of PV array output under different fault conditions were analyzed. The spatio-temporal composite function is constructed, and then used to set fuzzy rules for fault diagnosis of the PV array. Finally, an example is used to verify the effectiveness of the proposed method, and the results show that the method can describe the characters of spatial and temporal distribution of PV output under faults conditions, and it can effectively classify different faults.

The grade classification algorithm of breast tumor based on ultrasound RF signals

A novel efficient method based on the ultrasound radio frequency (RF) signals is proposed to distinguish the breast tumors grades. First, we utilize the multi-scale geometric characteristic of Shearlet transformation to extract the multi-scale and multi-directional features of ultrasound RF signal, and then reduce the high-dimensional Shearlet features by multi-scale directional binary pattern which can effectively preserve the sufficient discriminated information. At last, we draw on the feature difference between different grades of breast tumors to design a cascade binary tree SVM classifier which not only overcome the problem of sample quantity disequilibrium but also conform to the subjective diagnosis rule of sonographer. Extensive experiments on 928 breast ultrasound RF signals collected from the hospital demonstrate the effectiveness of the new proposed method and its precision, sensitivity, specificity, PPV, NPV and MCC are 89.29%, 75.62%, 94.54%, 97%, 98.3% and 81.01%, respectively.

Using data science to diagnose and characterize heterogeneity of Alzheimer's disease

IntroductionDespite the availability of age- and education-adjusted standardized scores for most neuropsychological tests, there is a lack of objective rules in how to interpret multiple concurrent neuropsychological test scores that characterize the heterogeneity of Alzheimer's disease. MethodsUsing neuropsychological test scores of 2091 participants from the Framingham Heart Study, we devised an automated algorithm that follows general diagnostic criteria and explores the heterogeneity of Alzheimer's disease. ResultsWe developed a series of stepwise diagnosis rules that evaluate information from multiple neuropsychological tests to produce an intuitive and objective Alzheimer's disease dementia diagnosis with more than 80% accuracy. DiscussionA data-driven stepwise diagnosis system is useful for diagnosis of Alzheimer's disease from neuropsychological tests. It demonstrated better performance than the traditional dichotomization of individuals' performance into satisfactory and unsatisfactory outcomes, making it more reflective of dementia as a spectrum disorder. This algorithm can be applied to both within clinic and outside-of-clinic settings.

An expert rule-based fault diagnosis strategy for variable refrigerant flow air conditioning systems

This paper proposed a novel fault diagnosis strategy of the variable refrigerant flow (VRF) system based on expert rules for the first time. The VRF fault diagnosis rules (VFDR) are obtained through the expert knowledge and characteristics of the VRF system. The VFDR includes a total of 22 expert rules, 10 rules for the outdoor unit and other 12 rules for the indoor unit. The proposed VFDR can help maintenance personnel to identify and eliminate VRF faults in time. In addition to obtaining the coefficients of the sensor regression model, the VFDR does not require a training process and is computationally simple, making it easily embedded in the building’s automatic control system to achieve online fault diagnosis. The proposed fault diagnosis strategy is validated with nine faults of the VRF system under cooling mode. These faults contain temperature sensor faults, the system fault and indoor unit faults. The diagnosis correct rate (DCR) is used to evaluate the performance of the VFDR. Through expert rules fault diagnosis strategy, the DCRs of all faults exceed 70% and the overall DCR of all faults is 85.13%. The results show that faults of VRF system are well diagnosed by fault diagnosis strategy based on expert rules.

APPLICATION OF THE INVERSE PROBLEM SOLUTIONS OF LINEAR AUTOREGRESSIVE PROCESSES FOR SIMULATION OF VIBRATION SIGNALS OF ROTOR PATS WIND POWER GENERATORS

Peculiar properties of vibration signals simulation of wind generators rotation nodes are considered. The application of vibration diagnosis as one of the most perspective diagnostic method of wind generators is justified. Advisability of statistical method application for the wind generators vibration diagnostics is proved. Mentioned, that effective vibration expert system designing is composed from several stages. First – mathematical model of node vibration with consideration of operation features should be elaborated. Second – it is necessary to develop hardware and software. Third – it is necessary to develop methods of decisive rules for wind generator nodes diagnosis. Fourth – it is necessary to carry out experimental verification of developed vibration diagnostic methods. Solution of inverse problem for autoregressive processes for the first time to apply for the problem solution. Generative process characteristic function finding method for linear autoregressive processes second-order AR(2) is used for the problem solution. Properties of characteristic functions of stationary linear autoregressive processes (AR) are discussed. The characteristic function may be represented as in canonical Kolmogorof form and in linear random process with discrete time form. Properties of transformation kernel and kernel of linear random process for the processes are also considered. Examples of Gamma second-order autoregressive generating process characteristic function determination are represented. Poisson jump spectra's properties of characteristic function of the linear autoregressive processes are also used for the solution of the problem. An example of application of vibration signal simulation of wind power generator is considered. Application of the results for characteristic function vibration signals of wind generator for modeling of the signals is shown. Features of application of the approach for modeling of rolling bearing vibration of wind generator USW 56-100 in frame of basic shift in radial direction near bearing at the side of wheel hub are considered. Rotation speed of wind wheel hub was equal 72 rpm. The experimental research was shown that vibration signals of the node may be simulated of second-order autoregressive process with Gamma distribution of generating process. Characteristic function of generating process for the autoregressive process was calculated. The article consideres examples of kernel of wing generation vibration signals when determination of inverse problem takes place and kernel of wing generation vibration signals when solution of inverse problem is impossible.

High-frequency ultrasound for diagnosing skin cancer in adults.

Early, accurate detection of all skin cancer types is essential to guide appropriate management and to improve morbidity and survival. Melanoma and squamous cell carcinoma (SCC) are high-risk skin cancers with the potential to metastasise and ultimately lead to death, whereas basal cell carcinoma (BCC) is usually localised, with potential to infiltrate and damage surrounding tissue. Anxiety around missing early curable cases needs to be balanced against inappropriate referral and unnecessary excision of benign lesions. Ultrasound is a non-invasive imaging technique that relies on the measurement of sound wave reflections from the tissues of the body. At lower frequencies, the deeper structures of the body such as the internal organs can be visualised, while high-frequency ultrasound (HFUS) with transducer frequencies of 20 MHz or more has a much lower depth of tissue penetration but produces a higher resolution image of tissues and structures closer to the skin surface. Used in conjunction with clinical and/or dermoscopic examination of suspected skin cancer, HFUS may offer additional diagnostic information compared to other technologies. To assess the diagnostic accuracy of HFUS to assist in the diagnosis of a) cutaneous invasive melanoma and atypical intraepidermal melanocytic variants, b) cutaneous squamous cell carcinoma (cSCC), and c) basal cell carcinoma (BCC) in adults. We undertook a comprehensive search of the following databases from inception up to August 2016: Cochrane Central Register of Controlled Trials; MEDLINE; Embase; CINAHL; CPCI; Zetoc; Science Citation Index; US National Institutes of Health Ongoing Trials Register; NIHR Clinical Research Network Portfolio Database; and the World Health Organization International Clinical Trials Registry Platform. We studied reference lists as well as published systematic review articles. Studies evaluating HFUS (20 MHz or more) in adults with lesions suspicious for melanoma, cSCC or BCC versus a reference standard of histological confirmation or clinical follow-up. Two review authors independently extracted all data using a standardised data extraction and quality assessment form (based on QUADAS-2). Due to scarcity of data and the poor quality of studies, we did not undertake a meta-analysis for this review. For illustrative purposes, we plot estimates of sensitivity and specificity on coupled forest plots. We included six studies, providing 29 datasets: 20 for diagnosis of melanoma (1125 lesions and 242 melanomas) and 9 for diagnosis of BCC (993 lesions and 119 BCCs). We did not identify any data relating to the diagnosis of cSCC.Studies were generally poorly reported, limiting judgements of methodological quality. Half the studies did not set out to establish test accuracy, and all should be considered preliminary evaluations of the potential usefulness of HFUS. There were particularly high concerns for applicability of findings due to selective study populations and data-driven thresholds for test positivity. Studies reporting qualitative assessments of HFUS images excluded up to 22% of lesions (including some melanomas) due to lack of visualisation in the test.Derived sensitivities for qualitative HFUS characteristics were at least 83% (95% CI 75% to 90%) for the detection of melanoma; the combination of three features (lesions appearing hypoechoic, homogenous and well defined) demonstrating 100% sensitivity in two studies (lower limits of the 95% CIs were 94% and 82%), with variable corresponding specificities of 33% (95% CI 20% to 48%) and 73% (95% CI 57% to 85%), respectively. Quantitative measurement of HFUS outputs in two studies enabled decision thresholds to be set to achieve 100% sensitivity; specificities were 93% (95% CI 77% to 99%) and 65% (95% CI 51% to 76%). It was not possible to make summary statements regarding HFUS accuracy for the diagnosis of BCC due to highly variable sensitivities and specificities. Insufficient data are available on the potential value of HFUS in the diagnosis of melanoma or BCC. Given the between-study heterogeneity, unclear to low methodological quality and limited volume of evidence, we cannot draw any implications for practice. The main value of the preliminary studies included may be in providing guidance on the possible components of new diagnostic rules for diagnosis of melanoma or BCC using HFUS that will require future evaluation. A prospective evaluation of HFUS added to visual inspection and dermoscopy alone in a standard healthcare setting, with a clearly defined and representative population of participants, would be required for a full and proper evaluation of accuracy.

Comparison of the value of contrast enhanced ultrasound and International Ovarian Tumor Analysis (IOTA) simple rules in the differential diagnosis of adnexal tumors

Objective To campare the diagnostic value of contrast-enhanced ultrasound(CEUS) and International Ovarian Tumor Analysis (IOTA) simple rules in the differential diagnosis of adnexal tumors. Methods For the 101 suspicious malignant adnexal tumors discovered by conventional ultrasound, IOTA simple rules and CEUS were performed before operation. Tumors were divided into benign, uncertain and malignant according to IOTA simple rules. Tumors were scored by CEUS according to the membrane integrity, coating thickness uniformity, separation enhancement, enhance strength(no enhancement, low enhancement, equal or high enhancement), and enhanced performance(uniform and non-uniform). The ROC curve was drawn with the pathological results as the gold standard, and the best boundary value was obtained. Finally, the diagnostic efficiency of the diagnostic methods were analyzed. Results ①According to the ROC curve analysis, the area under the curve of IOTA simple rules was 0.757(P=0.000). When the uncertainty type was classified as benign, the random index was 0.41, the sensitivity was 61.54%, and the specificity was 79.59%. When the uncertainty type was classified as malignant, the random index was 0.31, the sensitivity was 96.15%, and the specificity was 34.69%. When the uncertain types were excluded the Youden index was 0.77, the sensitivity was 76.19%, and the specificity was 89.47%. ②The differences in the membrane integrity, coating thickness uniformity, separation enhancement, enhance strength, enhanced performance between benign and malignant tumors were statistically significant(P<0.01). ③According to the ROC curve analysis, the area under the curve of CEUS was 0.914(P=0.000), cut-off value was 3 score. The Youden index, sensitivity and specificity of evaluating benign and malignant adnexal tumors were 0.80, 78.85% and 91.84% respectively. ④The tumors evaluated as benign by IOTA simple rules was classified as benign. The tumors evaluated as malignant by IOTA simple rules were classified as malignant. The tumors evaluated as uncertain by IOTA simple rules and scored less than or equal to 3 points according to CEUS were classified as benign. The tumors evaluated as uncertain by IOTA simple rules and scored more than 3 points according to CEUS were classified as malignant. According to the ROC curve analysis, the area under the curve of CEUS combined with IOTA simple rules was 0.831(P=0.000). The Youden index, sensitivity and specificity in evaluating benign and malignant adnexal tumors were respectively 0.66, 86.54% and 79.59% respectively. Conclusions CEUS has a higher diagnostic efficiency than IOTA Simple Rules and the two combined in the diagnosis of adnexal tumors. Key words: Contrast-enhanced ultrasound; Neoplasms, adnexal; IOTA Simple Rules

Vibration-Based Intelligent Fault Diagnosis for Roller Bearings in Low-Speed Rotating Machinery

This paper proposes a new signal feature extraction and fault diagnosis method for fault diagnosis of low-speed machinery. Statistic filter (SF) and wavelet package transform (WPT) are combined with moving-peak-hold method (M-PH) to extract features of a fault signal, and special bearing diagnostic symptom parameters (SSPs) in a frequency domain that are sensitive to bearing fault diagnosis are defined to recognize fault types. The SF is first used to adaptively cancel noises, and then fault detection is performed by exploiting the optimum symptom parameters in a time domain to identify a normal or fault state. For precise diagnosis, the SSPs are calculated after the signals are processed by M-PH and WPT. A decision tree is used to structure intelligent diagnosis rules in each step until the states are fully and automatically detected. The efficacy of this method was confirmed by applying it to an experimental low-speed rotation machine.

ABCD rule and pre-trained CNNs for melanoma diagnosis

Skin cancer is the most common type of cancer and represents more than half of cancer diagnoses. Melanoma is the least frequent among skin cancers, but it is the most serious, with high potential for metastasis and can lead to death. However, melanoma is almost always curable if discovered in the early stages. In this context, computational methods for processing and analysis of skin lesion images have been studied and developed. This work proposes a computational approach to assist dermatologists in the diagnosis of skin lesions in melanoma or non-melanoma by means of dermoscopic images. The proposed methodology classifies skin lesions using a descriptor formed by the combination of the ABCD rule (Asymmetry, Border, Color, and Diameter) and pre-trained Convolutional Neural Networks (CNNs) features. The features were selected according to their gain ratios and used as input to the MultiLayer Perceptron classifier. We built a new database joining two distinct databases presented in the literature to validate the proposed methodology. The proposed method achieved an accuracy rate of 94.9% and Kappa index of 89.2%, which is considered “excellent”.

Transformer fault diagnosis method based on graph theory and rough set

This paper combines the rough set with graph theory to deal with the problems of power transformer fault diagnosis, by the graph of decision table for fault diagnosis and its partitioned adjacency matrix. In the process, the new three-ratio decision table of fault diagnosis based on graph theory an d rough set is got without conflict and missing, to derive the new fault diagnosis rules. These new rules got by the partitioned core attribute of this graph can expand the fault diagnosis range of guideline IEC-60599, and improve the defect problem of three-radio fault diagnosis method. The results of experiment based on the 62 fault samples of power transformers prove the effectiveness of the new method.

Dynamic simulation of knowledge based reasoning of nuclear power plant operator in accident conditions: Modeling and simulation foundations

This paper describes major additions to the modeling and simulation capabilities of the Accident Dynamic Simulator paired with the Information, Decision, and Action in a Crew context (ADS-IDAC), a platform for conducting dynamic probabilistic risk assessment (DPRA) of nuclear power plants. The new advancements are mostly in modeling of operator knowledge-based behavior in accident conditions, enhancing realism of the IDAC model, and simulation approach to Human Reliability Analysis (HRA). The focus is situation assessment and diagnosis of the accident cause. Knowledge-based reasoning plays an important role in this phase. A reasoning module has been developed and implemented in ADS-IDAC to simulate operators’ knowledge-based reasoning. This paper describes the cognitive architecture of the reasoning module, including knowledge representation (model of operator’s understanding of the plant systems and functions), a memory representation, information processing flow, reasoning sequence generation, and rules for accident diagnosis. Some theoretical and empirical insights for human error prediction are embedded in this causal model as simulation rules. Human cognitive limitations and heuristics that potentially contribute to human errors are explicitly modeled. Together with the model description, several example simulations are provided to demonstrate different features of the reasoning module. Examples of the simulation show that the reasoning module in ADS-IDAC produces realistic knowledge-based responses by capturing cognitive limitations, deliberative reasoning, and dynamic of accident progression.

Traditional Chinese Medicine (TCM) Diagnosis Model Building Based on Multi-label Classification

In the study, we propose a TCM diagnosis model that can be used for multi-label classification and give clear diagnosis, as well as the basis for diagnosis and differentiation when the symptoms correspond to multiple diseases or syndromes. The implementation of the model is divided into three steps. Firstly, choose the machine learning algorithm to train the TCM diagnosis model. The features of the training data are symptoms and the labels are diseases or syndromes. Secondly, give the number α (α&gt;1, α∈Z+), the model will output the diagnoses with the top α highest probability according to the input symptoms as candidate diagnoses. Finally, the rules of differential diagnosis are designed to determine which candidate diagnoses should be reserved, thereby complete the multi-label classification. In our test dataset, by 10-fold cross-validation, the average accuracy of the single label classification was 0.882; the average precision was 0.974; the average recall was 1.000; the average f1 score was 0.967; the average accuracy of the multi-label classification was 0.706; the average micro precision was 0.934; the average micro recall was 0.941 and the average hamming loss was 0.060. Through the test we can know that this model had a good potential for auxiliary decision making in clinical diagnosis and treatment.