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

Quality 4.0 in action: Smart hybrid fault diagnosis system in plaster production

UIDB/00066/2020Industry 4.0 (I4.0) represents the Fourth Industrial Revolution in manufacturing, expressing the digital transformation of industrial companies employing emerging technologies. Factories of the future will enjoy hybrid solutions, while quality is the heart of all manufacturing systems regardless of the type of production and products. Quality 4.0 is a branch of I4.0 with the aim of boosting quality by employing smart solutions and intelligent algorithms. There are many conceptual frameworks and models, while the main challenge is to have the experience of Quality 4.0 in action at the workshop level. In this paper, a hybrid model based on a neural network (NN) and expert system (ES) is proposed for dealing with control chart patterns (CCPs). The idea is to have, instead of a passive descriptive model, a smart predictive model to recommend corrective actions. A construction plaster-producing company was used to present and evaluate the advantages of this novel approach, while the result shows the competency and eligibility of Quality 4.0 in action.publishersversionpublishe

ARTIFICIAL NEURAL NETWORK FOR AIR TRAFFIC CONTROLLER’S PRE-SIMULATOR TRAINING

Мета: Розробити нейромережеву модель оцінювання своєчасності та безпомилковості прийняття рішень спеціалістом з обслуговування повітряного руху в процесі передтренажерної підготовки. Методи: Дослідження базуються на основних положеннях концепції контролю факторів загроз та помилок при управлінні повітряним рухом, для характеристики складності ситуації (загроза-помилка-небажаний стан) використано методи експертних оцінок та теорії нечітких множин. Результати: Класифіковано етапи розвитку конфліктної ситуації та визначено кількісні показники рівня складності на кожному з етапів. Побудовано чотирьохшарову нейромережеву модель оцінювання своєчасності та безпомилковості прийняття рішень авіадиспетчером в процесі передтренажерної підготовки та отримано її параметри. Перший шар (вхідний) нейронної мережі представляє собою вправи, які виконують курсанти/слухачі, другий шар (схований) визначає психофізіологічні характеристики того, хто навчається, третій шар (схований) враховує складність вправи залежно від кількості потенційно конфліктних ситуацій, четвертий шар (вихідний) – оцінка курсанта/слухача при виконанні вправи. Нейромережева модель також має додаткові входи (зсув), що включають обмеження на обчислювальні параметри. За допомогою моделюючого комплексу Fusion отримано візуалізацію результатів виконання навчальної вправи авіадиспетчером за вказаними критеріями. Обговорення: Врахування критеріїв безпомилковості та своєчасності виконання поставлених інструктором завдань в процесі передтренажерного навчання за допомогою використання штучних нейронних мереж дозволить визначати можливість допуску спеціаліста з обслуговування повітряного руху до тренажерної підготовки. Мультимодульна система Fusion дасть можливість удосконалити процес професійної підготовки курсантів / слухачів – авіадиспетчерів завдяки автоматизації оцінювання їхніх дій.Цель: разработать нейросетевую модель оценивания своевременности и безошибочности принятия решений специалистом по обслуживанию воздушного движения в процессе предтренажерной подготовки. Методы: Исследования базируются на основных положениях концепции контроля факторов угроз и ошибок при управлении воздушным движением, для характеристики сложности ситуации (угроза-ошибка-нежелательное состояние) использованы методы экспертных оценок и теории нечетких множеств. Результаты: Классифицированы этапы развития конфликтной ситуации и определены количественные показатели уровня сложности на каждом этапе. Построена чотырехслойная нейросетевая модель оценивания своевременности и безошибочности принятия решений авиадиспетчером в процессе предтренажерной подготовки и получены ее параметры. Первый слой (входной) нейронной сети представляет собою упражнения, которые выполняют курсанты/слушатели, второй слой (скрытый) определяет психофизиологические характеристики обучаемого, третий слой (скрытый) учитывает сложность упражнения в зависимости от количества потенциально конфликтных ситуаций, четвертый слой (выходной) – оценка курсанта/слушателя при выполнении упражнения. Нейросетевая модель также имеет дополнительные входы (сдвиг), которые включают ограничения на вычисляемые параметры. С помощью моделирующего комплекса Fusіon получена визуализация результатов выполнения учебного упражнения авиадиспетчером за указанными критериями. Обсуждение: Учет критериев безошибочности и своевременности выполнения поставленных инструктором задач в процессе предтренажерного обучения с помощью использования искусственных нейронных сетей позволит определять возможность допуска специалиста по обслуживанию воздушного движения к тренажерной подготовке. Мультимодульная система Fusіon даст возможность усовершенствовать процесс профессиональной подготовки курсантов/слушателей – авиадиспетчеров благодаря автоматизации оценивания их действий.Purpose: to develop the neural network model for evaluating correctness and timeliness of decision-making by specialist of air traffic services during the pre-simulator training. Methods: researchers are based on the basic concepts of threat and error management in air traffic control, for characteristic of situation complexity (threat- error-undesirable condition) the methods of expert estimation and fuzzy sets theory have been used. Results: stages of the conflict situation developing have been classified and quantitative indicators of complexity level at each stage have been defined. Four layers neural network model for evaluating correctness and timeliness of decision-making by air traffic controller during the pre-simulator training has been built and its parameters have been obtained. The first layer (input) is exercises that perform cadets/listeners to solve potentially conflict situation, the second layer (hidden) is physiological characteristics of learner, the third layer (hidden) is the complexity of the exercise depending on the number of potentially conflict situations, the fourth layer (output) is assessment of cadets/listeners during performance of exercise. Neural network model also has additional inputs (Bias) that including restrictions on calculating parameters. With the help of modelling complex Fusion visualisation of results of educational task implementation by air traffic controller according to specified parameters have been defined. Discussion: taking into account timeliness and correctness of instructor’s tasks performance during the pre-simulator education with the help of using artificial neural networks will allow determining the possibility of access of specialist of air traffic services to simulator training. Multimodal system Fusion will give the possibility to improve the process of training of cadet's/listener's – air traffic controllers through automated assessment of their actions

Diagnosis of liver disease by computer- assisted imaging techniques: A literature review

Copyright © 2022 The authors. Diagnosis of liver disease using computer-aided detection (CAD) systems is one of the most efficient and cost-effective methods of medical image diagnosis. Accurate disease detection by using ultrasound images or other medical imaging modalities depends on the physician's or doctor's experience and skill. CAD systems have a critical role in helping experts make accurate and right-sized assessments. There are different types of CAD systems for diagnosing different diseases, and one of the applications is in liver disease diagnosis and detection by using intelligent algorithms to detect any abnormalities. Machine learning and deep learning algorithms and models play also a big role in this area. In this article, we tried to review the techniques which are utilized in different stages of CAD systems and pursue the methods used in preprocessing, extracting, and selecting features and classification. Also, different techniques are used to segment and analyze the liver ultrasound medical images, which is still a challenging approach to how to use these techniques and their technical and clinical effectiveness as a global approach

Return on Investment of the CFTP Framework With and Without Risk Assessment

In recent years, numerous high tech companies have developed and used technology roadmaps when making their investment decisions. Jay Paap has proposed the Customer Focused Technology Planning (CFTP) framework to draw future technology roadmaps. However, the CFTP framework does not include risk assessment as a critical factor in decision making. The problem addressed in this quantitative study was that high tech companies are either losing money or getting a much smaller than expected return on investment when making technology investment decisions. The purpose of this research was to determine the relationship between returns on investment before and after adding risk assessment to the CFTP framework. Paap\u27s CFTP framework and process to improve technology investments thus served as the theoretical framework for this study. Data were obtained from cloud computing companies using the companies\u27 market risk data and actual returns on investment data. The results and findings of paired sample two-tailed t tests for means and equal variances showed that return on investment was positively related to adding a traditional risk assessment model to Paap\u27s CFTP framework. These findings regarding the addition of risk assessment to the technology investment framework may be used by investors to (a) make better and more expeditious decisions, and (b) obtain a high return on technology investment by selecting the highest return value and lowest risk value

Predictive long-term asset maintenance strategy: development of a fuzzy logic condition-based control system

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceTechnology has accelerated the growth of the Facility Management industry and its roles are broadening to encompass more responsibilities and skill sets. FM budgets and teams are becoming larger and more impactful as new technological trends are incorporated into data-driven strategies. This new scenario has motivated institutions such as the European Central Bank to initiate projects aimed at optimising the use of data to improve the monitoring, control and preservation of the assets that enable the continuity of the Bank's activities. Such projects make it possible to reduce costs, plan, manage and allocate resources, reinforce the control, and efficiency of safety and operational systems. To support the long-term maintenance strategy being developed by the Technical Facility Management section of the ECB, this thesis proposes a model to calculate the Left wear margin of the equipment. This is accomplished through the development of an algorithm based on a fuzzy logic system that uses Python language and presents the system's structure, its reliability, feasibility, potential, and limitations. For Facility Management, this project constitutes a cornerstone of the ongoing digital transformation program

Control of the interaction of a gantry robot end effector with the environment by the adaptive behaviour of its joint drive actuators

The thesis examines a way in which the performance of the robot electric actuators can be precisely and accurately force controlled where there is a need for maintaining a stable specified contact force with an external environment. It describes the advantages of the proposed research, which eliminates the need for any external sensors and solely depends on the precise torque control of electric motors. The aim of the research is thus the development of a software based control system and then a proposal for possible inclusion of this control philosophy in existing range of automated manufacturing techniques.The primary aim of the research is to introduce force controlled behaviour in the electric actuators when the robot interacts with the environment, by measuring and controlling the contact forces between them. A software control system is developed and implemented on a robot gantry manipulator to follow two dimensional contours without the explicit geometrical knowledge of those contours. The torque signatures from the electric actuators are monitored and maintained within a desired force band. The secondary aim is the optimal design of the software controller structure. Experiments are performed and the mathematical model is validated against conventional Proportional Integral Derivative (PID) control. Fuzzy control is introduced in the software architecture to incorporate a sophisticated control. Investigation is carried out with the combination of PID and Fuzzy logic which depend on the geometrical complexity of the external environment to achieve the expected results

Performance Evaluation of Smart Decision Support Systems on Healthcare

Medical activity requires responsibility not only from clinical knowledge and skill but also on the management of an enormous amount of information related to patient care. It is through proper treatment of information that experts can consistently build a healthy wellness policy. The primary objective for the development of decision support systems (DSSs) is to provide information to specialists when and where they are needed. These systems provide information, models, and data manipulation tools to help experts make better decisions in a variety of situations. Most of the challenges that smart DSSs face come from the great difficulty of dealing with large volumes of information, which is continuously generated by the most diverse types of devices and equipment, requiring high computational resources. This situation makes this type of system susceptible to not recovering information quickly for the decision making. As a result of this adversity, the information quality and the provision of an infrastructure capable of promoting the integration and articulation among different health information systems (HIS) become promising research topics in the field of electronic health (e-health) and that, for this same reason, are addressed in this research. The work described in this thesis is motivated by the need to propose novel approaches to deal with problems inherent to the acquisition, cleaning, integration, and aggregation of data obtained from different sources in e-health environments, as well as their analysis. To ensure the success of data integration and analysis in e-health environments, it is essential that machine-learning (ML) algorithms ensure system reliability. However, in this type of environment, it is not possible to guarantee a reliable scenario. This scenario makes intelligent SAD susceptible to predictive failures, which severely compromise overall system performance. On the other hand, systems can have their performance compromised due to the overload of information they can support. To solve some of these problems, this thesis presents several proposals and studies on the impact of ML algorithms in the monitoring and management of hypertensive disorders related to pregnancy of risk. The primary goals of the proposals presented in this thesis are to improve the overall performance of health information systems. In particular, ML-based methods are exploited to improve the prediction accuracy and optimize the use of monitoring device resources. It was demonstrated that the use of this type of strategy and methodology contributes to a significant increase in the performance of smart DSSs, not only concerning precision but also in the computational cost reduction used in the classification process. The observed results seek to contribute to the advance of state of the art in methods and strategies based on AI that aim to surpass some challenges that emerge from the integration and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to quickly and automatically analyze a larger volume of complex data and focus on more accurate results, providing high-value predictions for a better decision making in real time and without human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações que os especialistas podem consistentemente construir uma política saudável de bem-estar. O principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações, modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores decisões em diversas situações. A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de diferentes fontes em ambientes de e-saúde, bem como sua análise. Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que podem suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional utilizado no processo de classificação. Os resultados observados buscam contribuir para o avanço do estado da arte em métodos e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados em inteligência artificial é possível analisar de forma rápida e automática um volume maior de dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana