Detection of chest pathologies using autocorrelation functions

An important feature of image analysis is texture, seen in all images, from aerial and satellite images to microscopic images in biomedical research. A chest X-ray is the most common and effective method for diagnosing severe lung diseases such as cancer, pneumonia, and tuberculosis. The lungs are the largest X-ray object. The correct separation of the shapes and sizes of the contours of the lungs is an important reason for diagnosis, because of which an intelligent information environment can be created. Despite the use of X-rays, to identify the diagnosis, there is a chance that the disease will not be detected. In this sense, there is a risk of development, which may be fatal. The article deals with the problems of pneumonia clustering using the autocorrelation function to obtain the most accurate result. This provides a reliable tool for diagnosing lung radiographs. Image pre-processing and data shaping play an important role in revealing a well-functioning basis of the nervous system. Therefore, images from two classes were selected for the task: healthy and with pneumonia. This paper demonstrates the applicability of the autocorrelation function for highlighting interest in lung radiographs based on the fineness of textural features and k-means extraction

Flow Dynamics in Cardiovascular Devices: A Comprehensive Review

This review explores flow dynamics in cardiovascular devices, focusing on fundamental fluid mechanics principles and normal blood flow patterns. It discusses the role of different structures in maintaining flow dynamics and the importance of stents, heart valves, artificial hearts, and ventricular assist devices in cardiovascular interventions. The review emphasizes the need for optimized designs and further research to enhance knowledge of flow dynamics in cardiovascular devices, advancing the field and improving patient care in cardiovascular interventions

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Qualitative modelling and simulation of physical systems for a diagnostic purpose

This is a Milton Keynes De Montfort University thesisThe goal of a fault-diagnosis system is to obtain an accurate diagnosis at a low cost. In order to reach this goal, many techniques have been used, e.g. qualitative methods and multiple-models. This research investigates a novel strategy for improving the balance accuracy versus cost of consistency-based fault-diagnosis systems. This new strategy is organised around the notion of entities. These are physical entities. such as water pressure or temperature. The functioning of a physical system can involve numerous entities. Because these entities influence each other's behaviour, multiple-fault situations can occur, where several entities are affected by a fault. These situations are called complex multiple-fault situations. The existing fault-diagnosis systems do not perform satisfactorily on complex multiple-fault situations. This is because the set of entities they investigate is fixed from the start of the diagnostic process. As a consequence, depending on the entities included in this set, existing systems either perform an inaccurate diagnosis, or reach an accurate diagnosis at an unnecessarily high cost. This thesis presents a fault-diagnosis strategy called MVDS (standing for Multiple Variable Diagnosis Strategy) designed specifically for performing the efficient diagnosis of complex multiple-fault situations. The underlying principle of MVDS is that it is not possible to know from the start of the diagnostic process which entities are affected. Thus, a diagnostic process with MVDS is undertaken with the investigation of an initial set of entities, and this set of investigated entities is continuously updated along the process, as intermediate results are obtained. In order to illustrate clearly the functioning of MVDS, a fault-scenario using a small example from the air-conditioning domain is diagnosed and the process studied. The investigation of the performance of MVDS on more complex physical systems is undertaken on a larger case-study using a hot-water and heating system. In MVDS, it is possible to disable the adaptability of the set of investigated entities, so that it can be run with a fixed set. By doing so, the performance of the strategy in MVDS can be compared to the performance of traditional approaches which use a fixed set of investigated entities. The study-case shows that MVDS reaches more accurate results than traditional approaches, and that this accuracy is obtained at a low cost, since unnecessary measurements of entities are avoided. Furthermore, the strategy produces a complete trace of the process that is close to common-sense reasoning. It is also a co-operative strategy where the operator can intervene. Summary of the main research contributions: - The issue of diagnosing complex multiple-fault situations is specifically addressed for the first time. The problem caused by this diagnosis task is defined, and a strategy is constructed in order to diagnose efficiently the complex multiple-fault situations. The strategy is implemented in MVDS and tested on an example and a case-study. - Risk characteristics have been described. They allow to evaluate how prone to complex muItiple-fault situations is a physical system. - Hot-water and heating systems are offered as a new domain of research for consistency-based fault-diagnosis systems. - The inclusion of co-operation into the fault-diagnosis process is a novel approach. Its potential advantages have been identified

Analysis of cardiac signals using spatial filling index and time-frequency domain

BACKGROUND: Analysis of heart rate variation (HRV) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system (ANS). HRV analysis is based on the concept that fast fluctuations may specifically reflect changes of sympathetic and vagal activity. It shows that the structure generating the signal is not simply linear, but also involves nonlinear contributions. These signals are essentially non-stationary; may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random in the time scale. However, to study and pinpoint abnormalities in voluminous data collected over several hours is strenuous and time consuming. METHODS: This paper presents the spatial filling index and time-frequency analysis of heart rate variability signal for disease identification. Renyi's entropy is evaluated for the signal in the Wigner-Ville and Continuous Wavelet Transformation (CWT) domain. RESULTS: This Renyi's entropy gives lower 'p' value for scalogram than Wigner-Ville distribution and also, the contours of scalogram visually show the features of the diseases. And in the time-frequency analysis, the Renyi's entropy gives better result for scalogram than the Wigner-Ville distribution. CONCLUSION: Spatial filling index and Renyi's entropy has distinct regions for various diseases with an accuracy of more than 95%

An embedded distributed tool for transportation systems health assessment

International audienceThis article presents an embedded distributed tool for health assessment of complex systems. The presented architecture is based on a solving method for embedded technical diagnostics and prognostics. This tool provides services enabling the evaluation of the health status of complex systems. Diagnostic services provide information for the maintenance decision support system that leads to reduce the periods of unavailability and determine if their future mission can be carried out. The diagnostic and prognostic functions are detailed and the exchanged data are specified. An example shows the feasibility of the proposed architecture and demonstrates the correctness of the developed algorithms

A Computuerized Operator Support System Prototype

A report was published by the Idaho National Laboratory in September of 2012, entitled Design to Achieve Fault Tolerance and Resilience, which described the benefits of automating operator actions for transients. The report identified situations in which providing additional automation in lieu of operator actions would be advantageous. It recognized that managing certain plant upsets is sometimes limited by the operator’s ability to quickly diagnose the fault and to take the needed actions in the time available. Undoubtedly, technology is underutilized in the nuclear power industry for operator assistance during plant faults and operating transients. In contrast, other industry sectors have amply demonstrated that various forms of operator advisory systems can enhance operator performance while maintaining the role and responsibility of the operator as the independent and ultimate decision-maker. A computerized operator support system (COSS) is proposed for use in nuclear power plants to assist control room operators in addressing time-critical plant upsets. A COSS is a collection of technologies to assist operators in monitoring overall plant performance and making timely, informed decisions on appropriate control actions for the projected plant condition. The COSS does not supplant the role of the operator, but rather provides rapid assessments, computations, and recommendations to reduce workload and augment operator judgment and decision-making during fast-moving, complex events. This project proposes a general model for a control room COSS that addresses a sequence of general tasks required to manage any plant upset: detection, validation, diagnosis, recommendation, monitoring, and recovery. The model serves as a framework for assembling a set of technologies that can be interrelated to assist with each of these tasks. A prototype COSS has been developed in order to demonstrate the concept and provide a test bed for further research. The prototype is based on four underlying elements consisting of a digital alarm system, computer-based procedures, PI&D system representations, and a recommender module for mitigation actions. At this point, the prototype simulates an interface to a sensor validation module and a fault diagnosis module. These two modules will be fully integrated in the next version of the prototype. The initial version of the prototype is now operational at the Idaho National Laboratory using the U.S. Department of Energy’s Light Water Reactor Sustainability (LWRS) Human Systems Simulation Laboratory (HSSL). The HSSL is a full-scope, full-scale glass top simulator capable of simulating existing and future nuclear power plant main control rooms. The COSS is interfaced to the Generic Pressurized Water Reactor (gPWR) simulator with industry-typical control board layouts. The glass top panels display realistic images of the control boards that can be operated by touch gestures. A section of the simulated control board was dedicated to the COSS human-system interface (HSI), which resulted in a seamless integration of the COSS into the normal control room environment

Application of Artificial Intelligence in Detection and Mitigation of Human Factor Errors in Nuclear Power Plants: A Review

Human factors and ergonomics have played an essential role in increasing the safety and performance of operators in the nuclear energy industry. In this critical review, we examine how artificial intelligence (AI) technologies can be leveraged to mitigate human errors, thereby improving the safety and performance of operators in nuclear power plants (NPPs). First, we discuss the various causes of human errors in NPPs. Next, we examine the ways in which AI has been introduced to and incorporated into different types of operator support systems to mitigate these human errors. We specifically examine (1) operator support systems, including decision support systems, (2) sensor fault detection systems, (3) operation validation systems, (4) operator monitoring systems, (5) autonomous control systems, (6) predictive maintenance systems, (7) automated text analysis systems, and (8) safety assessment systems. Finally, we provide some of the shortcomings of the existing AI technologies and discuss the challenges still ahead for their further adoption and implementation to provide future research directions