作者机构：
[Li Peng-cheng; Zhang Li; Dai Li-cao] Univ S China, Human Factor Inst, Hengyang 421001, Hunan, Peoples R China.;[Li Peng-cheng; Chen Guo-hua] S China Univ Technol, Inst Safety Sci & Engn, Guangzhou 510640, Guangdong, Peoples R China.
通讯机构：
[Li Peng-cheng] Univ S China, Human Factor Inst, Hengyang 421001, Hunan, Peoples R China.
关键词：
Human reliability analysis<&wdkj&>Bayesian network<&wdkj&>Human error<&wdkj&>Organizational factors<&wdkj&>Situational factors<&wdkj&>Individual factors
摘要：
Organizational factors are the major root causes of human errors, while there have been no formal causal model of human behavior to model the effects of organizational factors on human reliability. The purpose of this paper is to develop a fuzzy Bayesian network (BN) approach to improve the quantification of organizational influences in HRA (human reliability analysis) frameworks. Firstly, a conceptual causal framework is built to analyze the causal relationships between organizational factors and human reliability or human error. Then, the probability inference model for HRA is built by combining the conceptual causal framework with BN to implement causal and diagnostic inference. Finally, a case example is presented to demonstrate the specific application of the proposed methodology. The results show that the proposed methodology of combining the conceptual causal model with BN approach can not only qualitatively model the causal relationships between organizational factors and human reliability but also can quantitatively measure human operational reliability, and identify the most likely root causes or the prioritization of root causes causing human error.
摘要：
In the system reliability and safety assessment, the focuses are not only the risks caused by hardware or software, but also the risks caused by "human error". There are uncertainties in the traditional human error risk assessment (e.g. HECA) due to the uncertainties and imprecisions in Human Error Probability (HEP), Error-Effect Probability (EEP) and Error Consequence Severity (ECS). While fuzzy logic can deal with uncertainty and imprecision. It is an efficient tool for solving problems where knowledge uncertainty may occur. The purpose of this paper is to develop a new Fuzzy Human Error Risk Assessment Methodology (FHERAM) for determining Human Error Risk Importance (HERI) as a function of HEP, EEP and ECS. The modeling technique is based on the concept of fuzzy logic, which offers a convenient way of representing the relationships between the inputs (i.e. HEP, EEP, and ECS) and outputs (i.e. HERI) of a risk assessment system in the form of IF THEN rules. It is implemented on fuzzy logic toolbox of MATLAB using Mamdani techniques. A case example is presented to demonstrate the proposed approach. Results show that the method is more realistic than the traditional ones, and it is practicable and valuable. (C) 2010 Elsevier Ltd. All rights reserved.
作者：
Li Peng-cheng*;Zhang Li;Dai Li-cao;Li Xiao-fang
期刊：
Annals of Nuclear Energy,2017年102:168-178 ISSN：0306-4549
通讯作者：
Li Peng-cheng
作者机构：
[Li Peng-cheng; Zhang Li; Dai Li-cao; Li Xiao-fang] Univ South China, Human Factor Inst, Hengyang 421001, Hunan, Peoples R China.;[Li Peng-cheng] Univ South China, Sch Nucl Sci & Technol, Hengyang 421001, Hunan, Peoples R China.
通讯机构：
[Li Peng-cheng] Univ South China, Human Factor Inst, Hengyang 421001, Hunan, Peoples R China.
关键词：
Situation awareness<&wdkj&>Human error<&wdkj&>Analysis method<&wdkj&>Digitized main control rooms
摘要：
Situation awareness (SA) is a key element that impacts operator's decision-making and performance in nuclear power plants (NPPs). The subsequent complex cognitive activities cannot be correctly completed due to errors of situation awareness (ESA), which will lead to disastrous consequences. In order to investigate and analyze operator's ESA in the digitized main control room (DMCR) of a nuclear power plant, the model of ESA is established, the classification system of ESA is developed based on the built ESA model, and the analysis method of ESA is also constructed on the basis of the observation of simulator and operator surveys. Finally, a case study is provided to illustrate the concrete application of the method. It provides a theoretical and practical support for the operator's SAE analysis in a digitized main control room of a nuclear power plant. (C) 2016 Elsevier Ltd. All rights reserved.
摘要：
This paper proposes a novel H∞ tracking-based decentralized hybrid adaptive fuzzy controller via output feedback for a class of large-scale uncertain nonlinear systems. To achieve better adaptation properties, the proposed controller is a weighted combination of indirect and direct adaptive fuzzy controls (IAFC and DAFC) such that both fuzzy descriptions and control rules can be incorporated at the same time. After proper filtering of the observation error dynamics, the developed observer-based feedback and adaptation mechanisms become implementable indeed. By using fuzzy inference systems, a state observer and H∞ tracking technique, the decentralized combined indirect and direct adaptive fuzzy control algorithm is developed based upon a united H∞ tracking controller design for the modification of the previous work of Huang et al. The resultant closed-loop systems are guaranteed to be stable and a good H∞ tracking performance is obtained. Simulation results of interrelated inverted pendulums substantiate that the proposed CAFC system stands out as being better properties than typical IAFC and DAFC systems in that the tracking performance is much better while the control effort is even smaller.
期刊：
Mathematical Problems in Engineering,2015年2015 ISSN：1024-123X
通讯作者：
Zou, QM
作者机构：
[Zou, Qingming] Univ South China, Sch Econ & Management, Changsha 421001, Hunan, Peoples R China.;[Ye, Guangyu] S China Univ Technol, Sch Business Adm, Guangzhou 510640, Guangdong, Peoples R China.
通讯机构：
[Zou, QM ] Univ South China, Sch Econ & Management, Changsha 421001, Hunan, Peoples R China.;Univ South China, Sch Econ & Management, Changsha 421001, Hunan, Peoples R China.
通讯机构：
[Jiang, Jianjun] Univ South China, Human Factors Inst, Coll Econ & Management, Hengyang 421001, Hunan, Peoples R China.
关键词：
Digital human–computer interface<&wdkj&>Layout of relative positions<&wdkj&>Linear reversal genetic hybridization<&wdkj&>Human reliability<&wdkj&>Dynamic simulative function
摘要：
This is the first in a series of papers describing the optimal design method for a digital human-computer interface of a nuclear power plant (NPP) from three different points based on human reliability. The purpose of this series is to propose different optimization methods from varying perspectives to decrease human factor events that arise from the defects of a human–computer interface. The present paper mainly discusses the optimization method for the layout of monitoring units. The layout of relative positions among different functional blocks in a digital human–computer interface influences the time required to search information. The risk of an event increases with increases in the time required to search for information because of the limited time available during a nuclear emergency. To avoid the risk of such an event, the authors propose an optimization method for the layout of monitoring units based on human reliability for a digital human–computer interface of a NPP. In the optimal design process, the authors propose a linear reversal genetic hybridization method that uses the Bayesian method as an adaptive function and takes human reliability as the optimized criterion. To quantitatively obtain the probability of human reliability, the authors use dynamic simulative functions including time and human factors. Finally, an experiment is conducted. The results indicate that the linear reversal genetic hybridization method has good stability and sensitivity and that the proposed optimization method has good accuracy and convergence.
期刊：
Annals of Nuclear Energy,2018年120:48-61 ISSN：0306-4549
通讯作者：
Li, Peng-cheng
作者机构：
[Li, Xiao-fang; Li, Peng-cheng; Zhang, Li; Dai, Li-cao; Jiang, Yu] Univ South China, Human Factor Inst, Hengyang 421001, Hunan, Peoples R China.;[Li, Peng-cheng] Univ North Carolina Charlotte, Syst Engn & Engn Management, Charlotte, NC 28223 USA.;[Li, Peng-cheng] Univ South China, Sch Nucl Sci & Technol, Hengyang 421001, Hunan, Peoples R China.;[Zhang, Li] Hunan Inst Technol, Inst Human Factor & Safety Management, Hengyang 421002, Hunan, Peoples R China.
通讯机构：
[Li, Peng-cheng] Univ South China, Human Factor Inst, Hengyang 421001, Hunan, Peoples R China.
关键词：
Human error analysis<&wdkj&>Organizational root cause<&wdkj&>Human errors<&wdkj&>Performance shaping factors<&wdkj&>Digital nuclear power plants
摘要：
In nuclear power plants (NPPs), man-machine interfaces (MMIs) are being transformed from conventional panels to computer workstations. Therefore, operators' behavior and performance shaping factors (PSFs) in modern main control rooms (MCRs) differs from what they do in a conventional one. New human error, error mechanism and PSFs etc. are introduced into digital control systems (DCS). The conventional human error analysis (HEA) methods cannot meet new requirements. So it is important to establish a new technique of HEA for investigating human failure events in digital NPPs. In order to investigate and analyze human errors and to trace organizational root causes in digital NPPs, an Organization oriented Technique of Human Error Analysis (OTHEA) was established. At this first paper, an organization-oriented conceptual model of HEA was established. Furthermore, the classification framework of HEA was developed based on the established conceptual model of HEA, including classification of human error, PSFs, psychological error mechanisms (PEMs), error recovery failures (ERFs) and safety barriers. It provides a theoretical guidance and practical support for the investigation of human failure events in digital NPPs. (C) 2018 Elsevier Ltd. All rights reserved.
期刊：
Annals of Nuclear Energy,2017年106:247-255 ISSN：0306-4549
通讯作者：
Jiang, Jianjun
作者机构：
[Jiang, Jianjun; Zhang, Li] Hunan Inst Technol, Sch Safety & Environm Engn, Hengyang 421002, Hunan, Peoples R China.;[Jiang, Jianjun; Wang, Yiqun; Zhang, Mengjia; Xie, Tian] Univ South China, Sch Econ & Management, Human Factors Inst, Hengyang 421001, Hunan, Peoples R China.;[Li, Min; Wu, Daqing] Univ South China, Sch Comp Sci & Technol, Hengyang 421001, Hunan, Peoples R China.;[Peng, Yuyuan] South China Univ Technol, Guangzhou Coll, Sch Comp Engn, Guangzhou 510830, Guangdong, Peoples R China.;[Peng, Jie] GuangDong Univ Finance & Econ, Informat Sci Coll, Guangzhou 510320, Guangdong, Peoples R China.
通讯机构：
[Jiang, Jianjun] Hunan Inst Technol, Sch Safety & Environm Engn, Hengyang 421002, Hunan, Peoples R China.;[Jiang, Jianjun] Univ South China, Sch Econ & Management, Human Factors Inst, Hengyang 421001, Hunan, Peoples R China.
关键词：
Human reliability<&wdkj&>Quantity of components<&wdkj&>Quick convergence search method<&wdkj&>Affinity error probability mapping function
摘要：
This is the second in a series of papers describing the optimal design method for a digital human computer interface of nuclear power plant (Npp) from three different points based on human reliability. The purpose of this series is to explore different optimization methods from varying perspectives. This present paper mainly discusses the optimal design method for quantity of components of the same factor. In monitoring process, quantity of components has brought heavy burden to operators, thus, human errors are easily triggered. To solve the problem, the authors propose an optimization process, a quick convergence search method and an affinity error probability mapping function. Two balanceable parameter values of the affinity error probability function are obtained by experiments. The experimental results show that the affinity error probability mapping function about human-computer interface has very good sensitivity and stability, and that quick convergence search method for fuzzy segments divided by component quantity has better performance than general algorithm. (C) 2017 Elsevier Ltd. All rights reserved.
作者机构：
[Zhang, Ying; Zhang, Caiping] Univ South China, Sch Econ & Management, Hengyang, Peoples R China.;[Randhir, Timothy O.] Univ Massachusetts, Dept Environm Conservat, Amherst, MA 01003 USA.
通讯机构：
[Randhir, Timothy O.] Univ Massachusetts, Dept Environm Conservat, Amherst, MA 01003 USA.
关键词：
low-carbon transformation<&wdkj&>carbon assets<&wdkj&>indicator management method<&wdkj&>indicator system
摘要：
Effective carbon asset management is necessary for decreasing carbon resource inputs and reducing carbon emission. Furthermore, carbon asset management is an important measure to realize low-carbon transformation and low-carbon competitiveness for enterprises. However, a systematic theoretical framework and an effective management method for carbon asset management are lacking. Therefore, the paper defines carbon assets as tangible and intangible assets that can help reduce carbon emission and create low-carbon value based on the essence of carbon resources. In order to demonstrate a carbon asset's positive role in reducing carbon emission, the paper constructs carbon asset management indicators including carbon fund turnover ratio, carbon emission rate, carbon turnover ratio, carbon technology transformation efficiency and fixed carbon asset profitability. The carbon asset management practice of Bao Steel Corporation shows that the indicator system can accurately reflect the efficiency and effect of carbon asset management. That is, carbon fund turnover ratio can reflect a carbon fund's utilization efficiency; carbon emission rate reflects the carbon materials’ different effects on carbon emission; carbon turnover ratio reflects the profit of carbon trading; fixed carbon asset profitability reflects the low-carbon earning power to some degree; and carbon technology transformation efficiency reflects the maturity of low-carbon technology. However, with carbon asset management in the forefront of the carbon accounting field, some issues, including the nature of the carbon asset, management pattern and comparative analysis of more cases, need to be explored in future research to deepen the understanding of the essence of carbon asset management.
作者机构：
[Jiang, Jian-jun] Univ S China, Human Factors Inst, Econ Management Coll, Hengyang 421001, Hunan, Peoples R China.;[Zhang, Kun; Jiang, Jian-jun] Univ S China, Coll Nucl Sci & Technol, Hengyang 421001, Hunan, Peoples R China.;[Wang, Yi-qun] Univ S China, Ctr Res Informat Management, Hengyang 421001, Hunan, Peoples R China.;[Zhang, Li] HuNan Inst Technol, Hengyang 421001, Peoples R China.;[Jiang, Jian-jun; Peng, Yu-yuan] GuangZhou KangDa Vocat Tech Coll, Dept Comp Sci, Guangzhou 511363, Guangdong, Peoples R China.
通讯机构：
[Jiang, Jian-jun] Univ S China, Human Factors Inst, Econ Management Coll, Hengyang 421001, Hunan, Peoples R China.
关键词：
Digital main control room;Markov model;Probability distributed function;Probability of correlation degree
摘要：
Monitoring process is an important part in a high safety digital main control room of nuclear power plant (NPP), it is the source extracted information and found abnormal information in time. As the human factors events arisen from monitoring process recently take place more and more frequent, the authors propose a reliability Markov model to effectively decrease these abnormal events. The model mainly analyzes next monitoring object probability in terms of current information and plant state. The authors divide digital human-machine interface into two parts that are referred as logical homogeneous Markov and logical heterogeneous Markov. For the former, a series of methods of probability evaluation are proposed, such as, Markov transition probability with condition, probability distributed function with human factors, system state and alarm; for the latter, the authors propose the calculation of probability of correlation degree between last time and next time and probability calculation methods with multi-father nodes. The methods can effectively estimate the transition probability from a monitoring component to next monitoring component at time t, can effectively analyze which information is more important in next monitoring process and effectively find more useful information in time t + 1, so that the human factors events in monitoring process can greatly be decreased. (C) 2011 Elsevier Ltd. All rights reserved.
关键词：
Human factor events<&wdkj&>A weight association rules<&wdkj&>Dynamic function<&wdkj&>SGTR
摘要：
With human factor events rising in recent years, many researches begin to pay much attention to them. Especially, human factor events in nuclear power plant show more important than other human factor events. To effectively decrease human factor events, the authors propose the method of association rule analysis of human factor events in this paper. Association rule is one of the most popular data mining techniques applied to many scientific and industrial problems. Based on traditional methods, the authors propose a weight association rule based on statistics. Weight factors consist of inner and exterior human factors. In this paper, the authors propose a dynamic function and some methods with weight in order to assess support, confidence and correlation degree among human factor events. The proposed methods are tested by experiments. From results of experiments, we can easily find higher error rate events caused by human, higher confidence and correlation degree events among human factor events of steam generator tube rupture (SGTR) of nuclear power plant (NPP). (C) 2011 Elsevier Ltd. All rights reserved.
摘要：
To solve the problems of heterogeneity and disorder in the cross-domain integration processes for offsite nuclear emergency response, the integrated decision-making framework of the CDIRS spaces based on Space Mapping and Semantic Web is proposed. Under this framework, the method of feature mapping and space modeling, interoperable mediating and interoperability verifying, ordered integrating and reasoning, which are key steps of the CDIRS method, are presented. Furthermore, with a fictive nuclear accident scenario, the relevant CDIRS spaces and ordering rules are constructed, and then the appropriate cross-domain integrated response solution is obtained and verified effectively by reasoning and simulating in the CDIRS model. With the CDIRS method proposed in this paper, the heterogeneity and disorder can be avoided, and the targets and constraints of the offsite nuclear emergency response problems in the original space can be transformed into the target space, being processed unified by formal modeling and ordered reasoning. (C) 2016 Elsevier Ltd. All rights reserved.
作者机构：
[Zhang Li; Dai Licao; Li Pengcheng] Univ S China, Human Factor Inst, Hengyang 421001, Peoples R China.;[Dai Licao] Cent S Univ, Sch Infophys & Geomat Engn, Changsha 410083, Hunan, Peoples R China.;[Zhang Li] Hunan Inst Technol, Hengyang 421003, Peoples R China.
通讯机构：
[Dai Licao] Univ S China, Human Factor Inst, Hengyang 421001, Peoples R China.
关键词：
THERP + HCR<&wdkj&>HCR data modification<&wdkj&>HRA event tree<&wdkj&>Case study
摘要：
Human reliability analysis (HRA) is generally viewed as quite an important part in probabilistic safety assessment (PSA). In this paper, a THERP + HCR HRA model is presented to model the operators' post-accident behavior in Chinese nuclear power plants. The paper shows how the model is structured and how to consider and acquire the corresponding data, including HCR data modification and HRA event tree data. A case study is presented to make an illustration. (C) 2010 Elsevier Ltd. All rights reserved.
作者机构：
[Zhang, Li; Zou, Yanhua] Univ South China, Sch Nucl Sci & Technol, Hengyang 421001, Hunan, Peoples R China.;[Zhang, Li; Zou, Yanhua] Hunan Inst Technol, Inst Human Factors Engn & Safety Management, Hengyang 421002, Hunan, Peoples R China.;[Zhang, Li; Li, Pengcheng] Univ South China, Human Factor Inst, Hengyang 421001, Hunan, Peoples R China.
通讯机构：
[Zhang, Li] Hunan Inst Technol, Inst Human Factors Engn & Safety Management, Hengyang 421002, Hunan, Peoples R China.
关键词：
Situation assessment<&wdkj&>Dynamic network model<&wdkj&>Reliability forecasting<&wdkj&>Digital main control room<&wdkj&>Human performance
摘要：
With the technical development of computer hardware and software, digitalization is a trend in large-scale complex systems such as nuclear power plants (NPPs). It changes the way main control room (MCR) operators interact with systems. Faced with these technical changes, operators need to continue improving their situation assessment (SA) reliability level. In addition to evaluate operators' SA reliability, managers and shift supervisors also want to forecast their SA reliability level. There have been many studies with respect to operators' SA, but most of them are static analysis method and cannot be applied to predict operators' SA reliability. So, on the basis of different forecasting approaches and observation data, how to predict the operators' SA reliability level has became a problem that many analyst interest in. In this paper, first we identified the influence factors associated with SA reliability, and then we developed the SA reliability model, finally we proposed a reliability forecasting model by integrating time series forecasting method with dynamic network model (DNM). Our experiment verification focused on steam generator tube rupture (SGTR) event, using the forecasting model, we demonstrated how to predict operators' SA reliability during the course, and the prediction results are consistent with measurement results. (C) 2015 Elsevier Ltd. All rights reserved.
期刊：
Annals of Nuclear Energy,2017年109:82-91 ISSN：0306-4549
通讯作者：
Li, Peng-cheng
作者机构：
[Li, Peng-cheng; Li, Xiao-Fang; Zhang, Li; Dai, Li-cao] Univ South China, Human Factor Inst, Hengyang 421001, Hunan, Peoples R China.;[Li, Peng-cheng] Univ North Carolina Charlotte, Syst Engn & Engn Management, Charlotte, NC 28223 USA.;[Li, Peng-cheng] Univ South China, Sch Nucl Sci & Technol, Hengyang 421001, Hunan, Peoples R China.
通讯机构：
[Li, Peng-cheng] Univ South China, Human Factor Inst, Hengyang 421001, Hunan, Peoples R China.
关键词：
Situational awareness<&wdkj&>Reliability<&wdkj&>Digital main control room<&wdkj&>Nuclear power plant<&wdkj&>Bayesian theory
摘要：
Situational awareness (SA) is a key element impacting operators' and decision-making and performance in nuclear power plants. SA reliability is an important part of human reliability analysis. In order to quantitatively assess SA reliability, the Bayesian network model of operators' SA reliability analysis in digital main control rooms is built by data analysis. Furthermore, a quantitative assessment procedure of SA reliability is established, and the conditional probability distribution of influencing factors are more objectively determined on the basis of the full-size simulator experiment data of nuclear power plants and Bayesian theory. It is used to quantitatively calculate SA reliability, and a case is used to illustrate specific application of the established model. The results show that the model can provide more reliable data and standardized analysis procedures to support operators' SA reliability evaluation in digital nuclear power plants. (C) 2017 Elsevier Ltd. All rights reserved.
期刊：
Annals of Nuclear Energy,2017年109:185-191 ISSN：0306-4549
通讯作者：
Li, Peng-cheng
作者机构：
[Li, Xiao-fang; Li, Peng-cheng; Zhang, Li.; Dai, Li-cao] Univ South China, Human Factor Inst, Hengyang 421001, Hunan, Peoples R China.;[Li, Peng-cheng] Univ North Carolina Charlotte, Syst Engn & Engn Management, Charlotte, NC 28223 USA.;[Li, Peng-cheng] Univ South China, Sch Nucl Sci & Technol, Hengyang 421001, Hunan, Peoples R China.
通讯机构：
[Li, Peng-cheng] Univ South China, Human Factor Inst, Hengyang 421001, Hunan, Peoples R China.
关键词：
Situation awareness<&wdkj&>Causality model<&wdkj&>Human error<&wdkj&>Performance shaping factors<&wdkj&>Digital main control rooms
摘要：
This paper tries to overcome shortcomings of the traditional assessment method of situation awareness (SA) reliability and build more robust causality model of SA. Firstly, the organization-oriented analysis framework or method of SA error was used to analyze human factor events in nuclear power plants (NPPs) and the data of 132 samples were obtained. Then, the correlation analysis method is used to identify the correlation relationships between factors influencing SA and next factor analysis method is used to identify the scenes triggering SA error. It includes: operator’s mental level, operator's work attitude, stress level and system situation display level. Finally, based on the study of the results highlighted above, a data-driven SA causality model is established. These results shows that, the data-based SA causality model can identify the scenes triggering SA error, and it is very useful to improve the accuracy of quantitative assessment of SA reliability because of considering the causality relationships of performance shaping factors (PSFs).
