期刊：
Journal of Circuits, Systems and Computers,2024年 ISSN：0218-1266
通讯作者：
Li, PC
作者机构：
[Li, Pengcheng; Wen, Jing; Li, PC] Univ South China, Sch Resources & Environm & Safety Engn, Hengyang 421001, Hunan, Peoples R China.;[Wen, Jing] Hunan Inst Technol, Sch Design & Art, Hengyang 421002, Hunan, Peoples R China.;[Wang, Ye; He, Jiayuan; Li, Pengcheng; Wen, Jing; Li, PC] Univ South China, Inst Human Factors, Hengyang 421001, Hunan, Peoples R China.;[Tan, Haibo] China Nucl Power Engn Co Ltd, State Key Lab Nucl Power Safety Technol & Equipmen, Shenzhen 518172, Guangdong, Peoples R China.
通讯机构：
[Li, PC ] U;Univ South China, Sch Resources & Environm & Safety Engn, Hengyang 421001, Hunan, Peoples R China.;Univ South China, Inst Human Factors, Hengyang 421001, Hunan, Peoples R China.
关键词：
Team error;digital control rooms;nuclear power plants;performance influencing factors;human factors research
摘要：
To identify the shortcomings in current research on team errors and to outline future research directions aimed at enhancing safety management in the nuclear field, a review on team errors of key employees at nuclear power plants (NPPs) is compiled. The introduction of digital control systems in NPPs has fostered new human issues. To date, resources have focused on individual errors; however, the event investigations reveal that team errors significantly contribute to poor performance and accidents. The importance of team errors cannot be ignored, and incorporating team error control into safety systems is necessary for efficiently functioning NPPs. This study reviews fundamental aspects of team errors in digital control rooms of NPPs, covering three main foci: concept and classification of team errors, identification and classification of team error performance influencing factors (PIFs), and mechanisms of team errors. The main contributions of this review include the findings that (1) team interaction is not adequately captured in the definition of team errors; (2) the identification of team error patterns lacks an in-depth exploration of cognitive mechanisms, and the classification of team errors lacks uniform standards and objective methods; (3) 21 key PIFs leading to team errors have been identified, but the correlations between error patterns and PIFs have not been explored in depth; (4) lacking reliable systematic methods or models for analyzing team error mechanisms, especially in dealing with team dynamics and psychological error mechanisms. This research contributes to a more comprehensive, systematic understanding of team errors in NPPs, providing theoretical references for subsequent team error prevention and control strategy development.
作者：
Ye Wang;Huiwei Wei;Jing Wen;Jiayuan He;Pengcheng Li*
期刊：
Annals of Nuclear Energy,2024年211:110896 ISSN：0306-4549
通讯作者：
Pengcheng Li
作者机构：
[Ye Wang; Jiayuan He] School of Nuclear Science and Technology, University of South China, Hengyang, Hunan 421001, China;[Huiwei Wei] School of Economics, Management and Law, University of South China, Hengyang, Hunan 421001, China;[Jing Wen] School of Resources Environment and Safety Engineering, University of South China, Hengyang, Hunan 421001, China;[Pengcheng Li] Human Factor Institute, University of South China, Hengyang, Hunan 421001, China
通讯机构：
[Pengcheng Li] H;Human Factor Institute, University of South China, Hengyang, Hunan 421001, China
摘要：
The operational efficiency and developmental progress of nuclear power entities are significantly challenged by organizational vulnerability, which can lead to severe outcomes if neglected. This paper presents a systematic approach to identifying and quantifying the primary factors influencing organizational vulnerability within nuclear power plants (NPPs). An evaluative index is established, and an innovative hybrid methodology combining the Analytic Hierarchy Process (AHP) and fuzzy sets theory is applied to assess overall organizational vulnerability. A case study validates the approach, demonstrating low vulnerability and strong organizational reliability in the NPPs. The research contributes a valuable tool for enhancing safety and sustainability in the nuclear power sector.
The operational efficiency and developmental progress of nuclear power entities are significantly challenged by organizational vulnerability, which can lead to severe outcomes if neglected. This paper presents a systematic approach to identifying and quantifying the primary factors influencing organizational vulnerability within nuclear power plants (NPPs). An evaluative index is established, and an innovative hybrid methodology combining the Analytic Hierarchy Process (AHP) and fuzzy sets theory is applied to assess overall organizational vulnerability. A case study validates the approach, demonstrating low vulnerability and strong organizational reliability in the NPPs. The research contributes a valuable tool for enhancing safety and sustainability in the nuclear power sector.
作者机构：
[Licao Dai] Human Factor Institute, University of South China, Hengyang, China;[Chenze Zhuang] College of Computer Science, University of South China, Hengyang, China;[Qiang Fu] Department of Management Science and Engineering, University of South China, Hengyang, China
会议名称：
2024 IEEE 2nd International Conference on Control, Electronics and Computer Technology (ICCECT)
会议时间：
26 April 2024
会议地点：
Jilin, China
会议论文集名称：
2024 IEEE 2nd International Conference on Control, Electronics and Computer Technology (ICCECT)
摘要：
Emotion recognition technology based on electroencephalogram (EEG) signals has become a focal point for experts and scholars globally. To improve the accuracy of machine understanding of human emotions, this paper characterizes the multidimensional information features in EEG signals, and solves the problem of 4-dimensional fusion of temporal, frequency and 2D spatial features of EEG signals, and we present a novel method named 4D-CapsBLnet. First, we transformed the multidimensional features from various channels into 4D structures for training the deep learning model. Second, we utilized Capsule Networks (CapsNet) to capture spatial and frequency information from each temporal slice of 4D inputs, while Bidirectional Long Short- Term Memory Networks (Bi-LSTM) was employed to capture temporal dependencies. To comprehensively assess the model's performance, we conducted a five-fold cross-validation with a maximum epoch limit and early stopping strategy. The average accuracy and standard deviation on the SEED, SEED-IV, and SEED-V datasets are 95.06% ± 1.65%, 88.47% ± 1.74%, and 88.23% ± 1.12%, respectively. Compared to similar research methods, the proposed model in this paper demonstrates robustness and superior performance in emotion recognition tasks.
作者机构：
[Licao Dai] Human Factor Institute, University of South China, Hengyang, China;[Guangyu Liu; Jie Liu] School of Computer, University of South China, Hengyang, China
会议名称：
2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA)
会议时间：
24 February 2023
会议地点：
Changchun, China
会议论文集名称：
2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA)
关键词：
Operators in the digital main control rooms of nuclear power plants;Fatigue detection;Multi-feature Fusion;Machine Learning
摘要：
Timely waking up the operators in the digital main control rooms of nuclear power plants from fatigue can effectively prevent accidents in nuclear power plants. During the operator's work, real -time evaluation of the fatigue state of operators is very important. Because the operators needs to constantly receive and process information from the computer screen through their eyes, it is easy to make their eyes tired and causes mental fatigue to the operators, so we propose to conduct fatigue detection for the operators. By focusing on the state of eyes, combined with the state of mouth and head movement, the efficiency of fatigue detection for operators can be effectively improved. The fatigue detection method was designed to integrate the classification results of three machine learning classification algorithms, logistic regression (LR), random forest (RF), and support vector machine (SVM), by fusing multi- feature such as slow blinks, PERCLOS, yawns, and head movements. Through experimental comparison, this method has higher accuracy compared with the multi-feature fusion method also based on the NTHU-DDD video dataset.
作者机构：
[Zhang, Meihui; Li, Yu; Dai, Licao] Univ South China, Inst Human Factors, Hengyang 421001, Peoples R China.;[Li, Yu] Univ South China, Sch Comp Sci, Hengyang 421001, Peoples R China.;[Zhang, Meihui] Univ South China, Sch Nucl Sci & Technol, Hengyang 421001, Peoples R China.
通讯机构：
[Licao Dai] I;Institute of Human Factors, University of South China, Hengyang 421001, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
clustering;eyes’ blink rate;fatigue;mouse velocity;nuclear power plant main control room;PERCLOS;supervised learning
作者机构：
[常猛; 李鹏程] Nuclear and Radiation Safety Center, Ministry of Ecology and Environment, Beijing;100082, China;[王茹; 刘珍; 刘晓慧] Human Factor Institute, University of South China, Hunan, Hengyang;421001, China;[常猛] 100082, China
作者机构：
[Licao Dai] Human Factor Institute, University of South China, Hengyang, China;[Xiangyu Han] College of Computer Science, University of South China, Hengyang, China
会议名称：
2023 IEEE International Conference on Control, Electronics and Computer Technology (ICCECT)
会议时间：
28 April 2023
会议地点：
Jilin, China
会议论文集名称：
2023 IEEE International Conference on Control, Electronics and Computer Technology (ICCECT)
关键词：
Human factors engineering;Digital control system;Deep learning;Multimodal fusion;Fatigue recognition
摘要：
Fatigue recognition of operators in a complex human-machine interface can effectively avoid human error. Aiming at improving the operator's vigilance in the digital control room of nuclear power plants, this paper proposes a multimodal fatigue state recognition technique. It combines deep learning and eyelid feature information to recognize the fatigue state of operators. Temporal Convolutional Network (TCN) is integrated with the 3D Residual Network (ResNet3D) to retain the temporal and spatial features of the eyelid. By using a custom cosine annealing learning rate decay algorithm to avoid the training process of the model falling into the local optimum. In this study, experiments were conducted on a variety of eyelid feature information for different timesteps. The UTA-RLDD and DROZY datasets were used to train and validate the model. The experimental results show that the fusion recognition of eyelid images and eyelid aspect ratio (EAR) using ResNet3D and TCN multimodal model can achieve better recognition results. With the increase of the timestep, there is a significant increase in the effect of recognition of mental states. In addition, the study set up an eyelid fatigue dataset to better fit the working environment of the digital main control room in a nuclear power plant. When the timestep is set to 160, the recognition recall of the model for this test dataset is 88%. The multimodal model can detect the state of low vigilance and drowsiness in real-time. Compared with other methods, the recognition accuracy of the model is improved by an average of 2.25%.
作者：
LE, Trang Quang;WU, Wann-Yih;LIAO, Ying-Kai;PHUNG, Thuy Thi Thu
期刊：
Journal of Distribution Science (유통과학연구),2022年20(2):1-9 ISSN：1738-3110
通讯作者：
Phung, T.T.T.
作者机构：
[LE, Trang Quang; WU, Wann-Yih] Department of Business Administration, Nanhua University, Chiayi County, Taiwan;[LIAO, Ying-Kai] Program of International Business, Nanhua University, Chiayi County, Taiwan;[PHUNG, Thuy Thi Thu] Faculty of Sport Science, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
通讯机构：
Faculty of Sport Science, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
关键词：
Hedonic shopping value;Impulse buying behavior;Marketing stimuli;Meta-analysis;Online distribution channels;S-o-r
期刊：
Progress in Nuclear Energy,2022年144:104086 ISSN：0149-1970
通讯作者：
Pengcheng Li
作者机构：
[Luo, Zhuhua; Li, Pengcheng; Liu, Yahua; Dai, Licao] Univ South China, Sch Resources Environm & Safety Engn, Hengyang 421001, Hunan, Peoples R China.;[Jin, Xiao; Luo, Zhuhua; Li, Pengcheng; Liu, Yahua; Liu, Zhen; Dai, Licao] Univ South China, Human Factor Inst, Hengyang 421001, Hunan, Peoples R China.
通讯机构：
[Pengcheng Li] S;School of Resources, Environment and Safety Engineering, University of South China, Hengyang 421001, Hunan, People's Republic of China<&wdkj&>Human Factor Institute, University of South China, Hengyang 421001, Hunan, People's Republic of China
关键词：
Team situation awareness;Human reliability analysis;Dynamic Bayesian network;Digital nuclear power plants
摘要：
Team situation awareness (TSA) reliability is an important factor for team reliability. Moreover, situation awareness (SA) is a prominent problem in digital nuclear power plants (NPPs). Currently, since there is no suitable method to dynamically assess TSA reliability, we constructed a dynamical assessment method of TSA reliability based on a dynamic Bayesian network (DBN) to evaluate TSA reliability. First of all, a TSA causal concept model through qualitative analysis, expert group discussion and sample data analysis. On this basis, the quantitative assessment method of TSA reliability was constructed based on DBN and obtained probability distribution of variables. A standardized method was established to obtain the probability distribution of variables. Furthermore, we evaluated TSA dynamic reliability in a steam generator tube rupture (SGTR) accident. The results showed that the error probability of TSA decreased, and the level of TSA reliability continuously increased in SGTR. TSA reliability can be dynamically predicted by causal reasoning, the most important cause of TSA error could be identified by diagnostic reasoning, which provided theoretical support for the targeted prevention of human error. Finally, this established method was proved to be reasonable through sensitivity analysis.
作者：
NGUYEN, Phuong Minh Binh;DO, Yen Thi;WU, Wann Yih
期刊：
Journal of Asian Finance, Economics and Business,2021年8(6):1091-1099 ISSN：2288-4637
作者机构：
[NGUYEN, Phuong Minh Binh] Department of Hospitality Management, Faculty of Tourism, Van Lang University;[DO, Yen Thi; WU, Wann Yih] Department of Business Administration, College of Management, Nanhua University
关键词：
Social Media;Technology Acceptance Model;Knowledge Sharing Factors;Social Influence Factors
摘要：
The main objective of this study is to investigate the consumers' attitude and intention toward using social media by adopting the Technology Acceptance Model (TAM). This study further develops a comprehensive framework by identifying Knowledge Sharing Factors and Social Influence Factors as moderating variables that influence the relationship of attitude and behavior intention toward using social media. Based on the literature review, a research framework questionnaire is developed and conducted to test the research hypothesis in this study. The questionnaire survey method is employed to collect data from relevant social media, whereby 309 valid responses are used in the analysis. The results reveal that three TAM factors, namely, the impact of perceived usefulness, perceived ease of use, and perceived enjoyment are indeed the antecedents of attitude and behavior intention toward social media adoption. Also, the results indicate that social influence factors (social networking, social norms, and social trusts) and knowledge sharing factors (altruism, expected reciprocal benefit, and expected relationships) have moderating effect on the relationship between attitude and behavior intention toward social media. This research provides a comprehensive framework as important reference for professionals to develop social media marketing plan as well for academicians to conduct further validation.
