To make accurate and comprehensive evaluation of the catenary and diagnose the causes of the catenary fault, a method of catenary state evaluation and diagnosis based on the principal component analysis control chart was proposed, which can make full use of the multidimensional detection parameters of the catenary. The principal component analysis was used to reduce the dimension of catenary parameters, the principal component T2 control chart was calculated to show the change of principal component of catenary state data, the residual SPE control chart was calculated to show the change of their correlation, and the contribution rate control chart was calculated to show the cause of abnormal state data. The method can not only transform the multidimensional detection parameters of the catenary into a statistic to realize the simple and intuitive evaluation of the catenary state but also can accurately determine the cause of the abnormal state, so as to provide technical support for the targeted condition-based maintenance of the catenary.
Proper functions of Agni (fire) in the body signify good health of the individual while an abnormal state leads to manifestations of diseases. The word Jatara means Udara (abdomen) and the Agni located in Jatara is Jataragni (digestive fire) and its Pramana (quantity) differs in each organism. There are various anatomical structures relat- ed to Jataragni that contribute towards its normal functioning. The physiological process of digestion and metab- olism including biophysical and biochemical changes in the ingested food is carried out by the influence of Jata- ragni. It also influences the status of Dosha, Dhatu and Mala in the body. The objective of this study is to know the anatomical and physiological aspects of Jataragni to understand the pathological states in the body.
Keywords: Agni; Jataragni; Koshtanga; Grahani.
We discuss the problem of filtering out abnormal states from a larger number of quantum states. For this type of problem with [Formula: see text] items to be searched, both the traditional search by enumeration and classical Grover search algorithm have the complexity about [Formula: see text]. In this letter a novel quantum search scheme with exponential speed up is proposed for abnormal states. First, a new comprehensive quantum operator is well-designed to extract the superposition state containing all abnormal states with unknown number [Formula: see text] with complexity [Formula: see text] in probability 1 via well-designed parallel phase comparison. Then, every abnormal state is achieved respectively from [Formula: see text] abnormal states via [Formula: see text] times’ measurement. Finally, a numerical example is given to show the efficiency of the proposed scheme.
Abstract
The differences in the probability of occurrence of different equipment and defects lead to the small sample characteristics of the defect of the arrester, which makes it difficult to train an accurate prediction model. It is difficult to identify the abnormal state when the arrester monitoring data does not exceed the limit and increase steadily relying on the arrester monitoring index and threshold to judge the defect. Therefore, a lightning arrester defect early warning method based on multi-stage information and Bayesian inference is proposed. The Bayesian inference algorithm is used to calculate the probability of defect cause categories under different feature quantities. According to the new test evidence, the probability of the defect cause category under different feature quantities is updated to identify the defect cause. The algorithm automatically adjusts the prior probability indicators of equipment defects and causes in the model based on the new detection data and annotation conclusions to ensure the accuracy of defect cause classification. The lightning arrester operation and maintenance data and online monitoring system of a power company is used to analyze and verify the effectiveness and correctness of the method proposed in this paper, which provides effective supportfor the lightning arrester operation and maintenance.
For many disabled people, the "abnormal" state of things over the last year and a half is not such an estranged discontinuity from the previous state of things. Certainly, just like everyone, pandemic life for disabled people has been exceedingly difficult, painful, oppressive, and deadly. But the "normal" of pre-pandemic life was also exceedingly difficult, painful, oppressive, and deadly.
Context. The problem of identification a computer system state was investigated. The object of the research is the identification process of the computer system state. The subject of the research is computer system state identifying means and methods.
Objective. The purpose of the work is to develop a method for identifying the computer system state.
Method. The method has been developed for identifying a computer system state based on integrated use the procedure for grouping unlabeled initial data and using machine learning technology based on the «Isolation Forest» algorithm, which provides to identify a computer system state and to distinguished the process name that initiated the abnormal state. Therefore, for collecting statistical data in the form of operating system functioning events, data method has been proposed and developed along with software. The analysis of functioning events has been performed. The result of analysis showed that the most informative are read and write operations. To set up a single dataset, read and write operations compared with the process name and combined into one array of event groups, so that it is possible to single out the process that causes the abnormal state of the computer system. As a result of the research, the «Isolation Forest» algorithm has been selected as a component of the method for identifying the computer system state. An accuracy and efficiency assessment of the developed method of identifying a computer system state has been carried out.
Results. The developed method is implemented and investigated when solving the problem of identifying anomalies in the functioning of computer systems.
Conclusions. The experiments carried out confirmed the efficiency of the proposed method. It allows us recommended the method for practical use in order to improve efficiency of identifying the computer system state and use it as an express method. Areas for further research may lie in the creation of the ensemble of fuzzy trees based on the proposed method and optimization of this software implementation.
The work state of a launch vehicle is generally interpreted automatically on software. However, the sheer number of target parameters makes it difficult to realize real-time interpretation, and abnormal interpretation result does not necessarily mean that the vehicle is in abnormal state. This paper introduces the edge computing to achieve on-line interpretation and real-time diagnosis of a single launch vehicle. Firstly, the parameters to be interpreted were subjected to thresholding, leaving only those with high interpretation value. Next, the interpretation server layer of the real-time diagnosis model was built based on the attribute and value reduction algorithm of variable precision rough set (VPRS). Moreover, the higher-grade criteria were written in criterion modeling language (CML) and used to interpret the various higher-grade interpretation data pushed by the edge layer in real time. On this basis, the outputs of the edge layer and interpretation server layer were integrated to achieve the real-time diagnosis of single vehicle faults. Finally, the proposed model was proved feasible through the application in a launch vehicle.
A Principal Component Analysis Control Chart Method for Catenary Status Evaluation and Diagnosis