Publications by authors named "Hanbin Luo"

An electroencephalogram (EEG)-based brain-computer interface (BCI) speller allows a user to input text to a computer by thought. It is particularly useful to severely disabled individuals, e.g. amyotrophic lateral sclerosis patients, who have no other effective means of communication with another person or a computer. Most studies so far focused on making EEG-based BCI spellers faster and more reliable; however, few have considered their security. This study, for the first time, shows that P300 and steady-state visual evoked potential BCI spellers are very vulnerable, i.e. they can be severely attacked by adversarial perturbations, which are too tiny to be noticed when added to EEG signals, but can mislead the spellers to spell anything the attacker wants. The consequence could range from merely user frustration to severe misdiagnosis in clinical applications. We hope our research can attract more attention to the security of EEG-based BCI spellers, and more broadly, EEG-based BCIs, which has received little attention before.

Estimation of construction waste generation (CWG) at the field scale is a crucial but challenging task for effective construction waste management (CWM). Extant field-scale CWG modeling approaches have faced difficulties in obtaining accurate results due to a lack of detailed CWG data, and most of them fail to consider the complex relationship among predictive variables. This study attempts to tackle this issue by proposing a novel CWG modeling approach that integrates improved on-site measurement (IOM) and a support vector machine (SVM)-based prediction model. To achieve this goal, 206 ongoing commercial construction sites were investigated to obtain the predictor values and waste generation rates (WGRs) of five types of waste (i.e., inorganic nonmetallic waste, organic waste, metal waste, composite waste, and hazardous waste) generated at three construction stages (i.e., the understructure stage, superstructure stage, and finishing stage). The data were introduced to the SVM to develop the relationships between predictive variables and WGRs. An actual commercial building under construction was used to demonstrate the applicability of the proposed approach. The results showed that the superiority of the IOM can be used as a basis to implement robust CWG data collection. In addition, the SVM-based WGR prediction model (SWPM) can obtain more accurate prediction results (R = 86.87%) than the back-propagation neural network (R = 75.14%) and multiple linear regression (R = 61.93%).

With the outbreak of the 2019 novel coronavirus (COVID-19) epidemic in Wuhan, China, in January 2020, the escalating number of confirmed and suspected cases overwhelmed the admission capacity of the designated hospitals. Two specialty field hospitals- and -were designed, built and commissioned in record time (9-12 days) to address the outbreak. This study documents the design and construction of Hospital. Based on data collected from various sources such as the semi-structured interviews of key stakeholders from Hospital, this study found that adhering to a product, organization, and process (POP) modeling approach combined with building information modeling (BIM) allowed for the ultra-rapid creation, management, and communication of project-related information, resulting in the successful development of this fully functional, state-of-the-art infectious disease specialty hospital. With the unfortunate ongoing international COVID-19 outbreak, many countries and regions face similar hospital capacity problems. It is thus expected that the lessons learned from the design, construction and commissioning of Hospital can provide a valuable reference to the development of specialty field hospitals in other countries and regions.

Construction workers' reactions to safety-related issues during operation vary from person to person due to their different occupational levels, which can be attributed to various influencing factors and their correspondingly complicated interactions. This research aims to propose an integrated framework to combine the concepts of these factors and provide a holistic interpretation of the interrelationship among them. Based on items that were mainly extracted from competency theory, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted to identify the critical factors from the data collected from 243 scaffolders on Wuhan Metro construction sites. The interactions among the identified factors were then analyzed, and the safety competency model was thus established with the use of structural equation modeling (SEM). A total of 17 items were identified as critical to workers' safety competency, and these were further tested and attributed to four factors: (1) individual character and inclination; (2) self-adjustment and adaptability; (3) working attitudes; (4) safety-related operation qualification. Subsequent analysis showed that all the factors significantly contributed to one's safety competency, and individual character and inclination contributed most to the formation of one's ability, while the intermediating effects of self-adjustment and adaptability should not be neglected both in theoretical and practical terms. The resultant safety competency model consisting of these four factors was revealed to share a hierarchical structure with the classical competency model. This study provided an integrated theoretical framework and a set of modeling approaches to combine the related concepts and facilitate a greater understanding of construction safety in terms of workers' characteristics and behaviors. This study presented a tentative approach for assessing construction workers' safety competency, as well as emphasized to the managers and professionals the necessity of developing training systems to ensure workers are integrated into a crew in an appropriate and smooth manner. The volume and the scope of samples impeded the study from achieving a more generalized result and a more cost-efficient data collection approach is in need of development for a comprehensive and in-depth investigation.

This study aimed to reveal opinion leaders who could impact their coworkers' safety-related performance in Chinese construction teams. Questionnaires were distributed to 586 scaffolders in Wuhan to understand their opinions about influencing their coworkers, serving as the foundation for a social network analysis to identify the potential opinion leaders among workers. A further controlled trial with the identified workers was conducted to select real opinion leaders by comparing their influence on others' safety-related behavior, followed by an association analysis to profile these opinion leaders. Two main sources of opinion leaders were identified: foremen and seasoned workers. Implementing interventions through opinion leaders resulted in better safety-related behavior performance. Furthermore, compared with education level, the association analysis results indicated that one's practical skills and familiarity with respondents was more important in the formulation of opinion leaders. This research introduces the concept of opinion leaders into construction safety and proposes an approach to identify and validate opinion leaders within a crew, thus providing a tool to improve behavior promotion on sites, as well as a new perspective for viewing interactions among workers.