In the past, several firefighters have died in disaster relief operations. Although the firefighters were fully equipped, the scene of the disaster was smoky and disorienting, making the firefighters unable to identify their location. The commander wanted to direct the firefighters outside but could not confirm the correct location of the firefighters, causing delays in rescue. GPS cannot support indoor positioning or preset indoor positioning facilities at the moment of fire extinguishing. However, geomagnetism is everywhere, and it can be used to identify one’s location. Unfortunately, due to the uncertainty of the magnetic field strength, indoor geomagnetism is affected by the building environment, and the existing magnetic positioning methods have difficulty obtaining a location. To solve this problem, we propose a new incremental indoor localization scheme based on the difference in geomagnetic intensity. The proposed method achieves indoor localization in 2D environments successfully. The novelty of our geomagnetic indoor positioning system is that it can perform indoor positioning without adding any indoor positioning facilities, and the accuracy can reach 0.8~1.5 m. This article aims to verify that the geomagnetic turbulence filtering algorithm can filter out abnormal geomagnetic intensity, that the incremental algorithm can estimate the position of human motion, and that geomagnetism can be used for indoor positioning without any preset infrastructure. The contribution of this paper is that we have developed a practical system that can be used without any infrastructure and can be used for indoor positioning with meter-level accuracy. The geomagnetic indoor positioning system can be integrated with a wireless network and applied to disaster relief.

This study investigates how Web users build reliance on information disclosed by the financial service industry. Unlike most prior studies that focused on business' incentives to dismiss information, this study collects Web users' perceptions, using survey questionnaires on six information categories. The results show that riskmanagement-related information is positively related to Web user's reliance. These findings imply that the financial service industry can rebuild public reliance through more transparent reporting on information regarding risk, especially after the turmoil resulting from the recent financial crisis.

This study investigates the perception of factors that are likely to influence the Internet information relevance of financial institutions. The media richness theory and stakeholder theory point out that the extent of information and stakeholder communication will influence the utilization of online information, while not many of these discussions are associated with financial institutions. This study integrates seven information categories into three constructs: sufficiency, stakeholder communication and external supervision, using a structural equation model to examine whether the three constructs relate to information utilization. The results show that stakeholder communication and external oversight are significantly related to the user's intention to utilize online information of financial institutions, while information sufficiency is not. These findings correspond to stakeholder theory as well as international guidelines that emphasize external oversight of financial institutions.

Post-COVID-19, there are frequent manpower shortages across industries. Many factories pursuing future technologies are actively developing smart factories and introducing automation equipment to improve factory manufacturing efficiency. However, the delay and unreliability of existing wireless communication make it difficult to meet the needs of AGV navigation. Selecting the right sensor, reliable communication, and navigation control technology remains a challenging issue for system integrators. Most of today’s unmanned vehicles use expensive sensors or require new infrastructure to be deployed, impeding their widespread adoption. In this paper, we have developed a self-learning and efficient image recognition algorithm. We developed an unmanned vehicle system that can navigate without adding any specialized infrastructure, and tested it in the factory to verify its usability. The novelties of this system are that we have developed an unmanned vehicle system without any additional infrastructure, and we developed a rapid image recognition algorithm for unmanned vehicle systems to improve navigation safety. The core contribution of this system is that the system can navigate smoothly without expensive sensors and without any additional infrastructure. It can simultaneously support a large number of unmanned vehicle systems in a factory.