Human Error Management Paying Emphasis on Decision Making and Social Intelligence -Beyond the Framework of Man-Machine Interface Design-

How latent error or violation induces a serious accident has been reviewed and a proper addressing measure of this has been proposed in the framework of decision making, emotional intelligence (EI) and social intelligence (SI) of organization and its members. It has been clarified that EI and SI play an important role in decision making. Violations frequently occur all over the world, although we definitely understand that we should not commit violations, and a secret to prevent this might exist in the enhancement of both social intelligence and reliability. The construction of social structure or system that supports organizational efforts to enhance both social intelligence and reliability would be essential. Traditional safety education emphasizes that it is possible to change attitudes or mind toward safety by means of education. In spite of this，accidents or scandals frequently occur and never decrease. These problems must be approached on the basis of the full understanding of social intelligence and limited reasonability in decision making. Social dilemma (We do not necessarily cooperate in spite of understanding its importance, and we sometimes make decision not to select cooperative behavior. Non-cooperation gives rise to a desirable result for an individual. However, if all take non-cooperative actions, undesirable results are finally induced to all.) must be solved in some ways and the transition from relief (closed) society to global (reliability) society must be realized as a whole. New social system, where cooperative relation can be easily and reliably obtained, must be constructed to support such an approach and prevent violation-based accidents

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

Consistency Index-Based Sensor Fault Detection System for Nuclear Power Plant Emergency Situations Using an LSTM Network

A nuclear power plant (NPP) consists of an enormous number of components with complex interconnections. Various techniques to detect sensor errors have been developed to monitor the state of the sensors during normal NPP operation, but not for emergency situations. In an emergency situation with a reactor trip, all the plant parameters undergo drastic changes following the sudden decrease in core reactivity. In this paper, a machine learning model adopting a consistency index is suggested for sensor error detection during NPP emergency situations. The proposed consistency index refers to the soundness of the sensors based on their measurement accuracy. The application of consistency index labeling makes it possible to detect sensor error immediately and specify the particular sensor where the error occurred. From a compact nuclear simulator, selected plant parameters were extracted during typical emergency situations, and artificial sensor errors were injected into the raw data. The trained system successfully generated output that gave both sensor error states and error-free states

Cross-comparative analysis of evacuation behavior after earthquakes using mobile phone data

Despite the importance of predicting evacuation mobility dynamics after large scale disasters for effective first response and disaster relief, our general understanding of evacuation behavior remains limited because of the lack of empirical evidence on the evacuation movement of individuals across multiple disaster instances. Here we investigate the GPS trajectories of a total of more than 1 million anonymized mobile phone users whose positions are tracked for a period of 2 months before and after four of the major earthquakes that occurred in Japan. Through a cross comparative analysis between the four disaster instances, we find that in contrast with the assumed complexity of evacuation decision making mechanisms in crisis situations, the individuals' evacuation probability is strongly dependent on the seismic intensity that they experience. In fact, we show that the evacuation probabilities in all earthquakes collapse into a similar pattern, with a critical threshold at around seismic intensity 5.5. This indicates that despite the diversity in the earthquakes profiles and urban characteristics, evacuation behavior is similarly dependent on seismic intensity. Moreover, we found that probability density functions of the distances that individuals evacuate are not dependent on seismic intensities that individuals experience. These insights from empirical analysis on evacuation from multiple earthquake instances using large scale mobility data contributes to a deeper understanding of how people react to earthquakes, and can potentially assist decision makers to simulate and predict the number of evacuees in urban areas with little computational time and cost, by using population density information and seismic intensity which can be observed instantaneously after the shock

Using graphical models and multi-attribute utility theory for probabilistic uncertainty handling in large systems, with application to nuclear emergency management

Although many decision-making problems involve uncertainty, uncertainty handling within large decision support systems (DSSs) is challenging. One domain where uncertainty handling is critical is emergency response management, in particular nuclear emergency response, where decision making takes place in an uncertain, dynamically changing environment. Assimilation and analysis of data can help to reduce these uncertainties, but it is critical to do this in an efficient and defensible way. After briefly introducing the structure of a typical DSS for nuclear emergencies, the paper sets up a theoretical structure that enables a formal Bayesian decision analysis to be performed for environments like this within a DSS architecture. In such probabilistic DSSs many input conditional probability distributions are provided by different sets of experts overseeing different aspects of the emergency. These probabilities are then used by the decision maker (DM) to find her optimal decision. We demonstrate in this paper that unless due care is taken in such a composite framework, coherence and rationality may be compromised in a sense made explicit below. The technology we describe here builds a framework around which Bayesian data updating can be performed in a modular way, ensuring both coherence and efficiency, and provides sufficient unambiguous information to enable the DM to discover her expected utility maximizing policy

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Alternative sweetener from curculigo fruits

This study gives an overview on the advantages of Curculigo Latifolia as an alternative sweetener and a health product. The purpose of this research is to provide another option to the people who suffer from diabetes. In this research, Curculigo Latifolia was chosen, due to its unique properties and widely known species in Malaysia. In order to obtain the sweet protein from the fruit, it must go through a couple of procedures. First we harvested the fruits from the Curculigo trees that grow wildly in the garden. Next, the Curculigo fruits were dried in the oven at 50 0C for 3 days. Finally, the dried fruits were blended in order to get a fine powder. Curculin is a sweet protein with a taste-modifying activity of converting sourness to sweetness. The curculin content from the sample shown are directly proportional to the mass of the Curculigo fine powder. While the FTIR result shows that the sample spectrum at peak 1634 cm–1 contains secondary amines. At peak 3307 cm–1 contains alkynes

原子力プラントのライフサイクル情報管理に基づく廃炉シミュレーションに関する研究

Tohoku University博士（情報科学）thesi