• International journal of advanced smart convergence
• /
• v.7 no.4
• /
• pp.75-83
• /
• 2018

Predictive IoT Sensor Algorithm is a technique of data science that helps computers learn from existing data to predict future behaviors, outcomes, and trends. This algorithm is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions. Sensors and computers collect and analyze data. Using the time series prediction algorithm helps to predict future temperature. The application of this IoT in industrial environments like power plants and factories will allow organizations to process much larger data sets much faster and precisely. This rich source of sensor data can be networked, gathered and analyzed by super smart software which will help to detect problems, work more productively. Using predictive IoT technology - sensors and real-time monitoring - can help organizations exactly where and when equipment needs to be adjusted, replaced or how to act in a given situation.

• Journal of Information Processing Systems
• /
• v.15 no.5
• /
• pp.1218-1230
• /
• 2019

This study proposes a novel video traffic flow detection method based on machine vision technology. The three-frame difference method, which is one kind of a motion evaluation method, is used to establish initial background image, and then a statistical scoring strategy is chosen to update background image in real time. Finally, the background difference method is used for detecting the moving objects. Meanwhile, a simple but effective shadow elimination method is introduced to improve the accuracy of the detection for moving objects. Furthermore, the study also proposes a vehicle matching and tracking strategy by combining characteristics, such as vehicle's location information, color information and fractal dimension information. Experimental results show that this detection method could quickly and effectively detect various traffic flow parameters, laying a solid foundation for enhancing the degree of automation for traffic management.

• Journal of Advanced Marine Engineering and Technology
• /
• v.27 no.5
• /
• pp.575-580
• /
• 2003

• International Journal of Control, Automation, and Systems
• /
• v.3 no.spc2
• /
• pp.259-261
• /
• 2005

• 제어로봇시스템학회:학술대회논문집
• /
• 1989.10a
• /
• pp.896-900
• /
• 1989

• Journal of Information Management
• /
• v.10 no.5
• /
• pp.162-167
• /
• 1977

• Fiber Technology and Industry
• /
• v.2 no.1
• /
• pp.107-112
• /
• 1998

• Journal of Drive and Control
• /
• v.21 no.1
• /
• pp.65-69
• /
• 2024

• 제어로봇시스템학회:학술대회논문집
• /
• 1991.10b
• /
• pp.1639-1645
• /
• 1991

Because of the important role LD converters play in the production of high quality steel, various dynamic models have been attempted in the past by many researchers not only to understand the complex chemical reactions that take place in the converter process but also to assist the converter operation itself using computers. And yet no single dynamic model was found to be completely satisfactory because of the complexity involved with the process. The process indeed involves dynamic energy and mass balances at high temperatures accompanied by complex chemical reactions and transport phenomena in the molten state. In the present study, a mathematical model describing the dynamic behavior of LD converter process has been developed. The dynamic model describes the time behavior of the temperature and the concentrations of chemical species in the hot metal bath and slag. The analysis was greatly facilitated by dividing the entire process into three zones according to the physical boundaries and reaction mechanisms. These three zones were hot metal (zone 1), slag (zone 2) and emulsion (zone 3) zones. The removal rate of Si, C, Mn and P and the rate of Fe oxidation in the hot metal bath, and the change of composition in the slag were obtained as functions of time, operating conditions and kinetic parameters. The temperature behavior in the metal bath and the slag was also obtained by considering the heat transfer between the mixing and the slag zones and the heat generated from chemical reactions involving oxygen blowing. To identify the unknown parameters in the equations and simulate the dynamic model, Hooke and Jeeves parttern search and Runge-Kutta integration algorithm were used. By testing and fitting the model with the data obtained from the operation of POSCO #2 steelmaking plant, the dynamic model was able to predict the characteristics of the main components in the LD converter. It was possible to predict the optimum CO gas recovery by computer simulation

• Proceedings of the KIEE Conference
• /
• 2002.07a
• /
• pp.6-8
• /
• 2002

In this paper present Autonomous Adaptive Digital Over Current Relay which acts autonomous setting using voltage and current measured by relay in the change of power systems. Automation of relay setting is required for distribution automation although manual relay setting is used at present. This paper presents Autonomous Relay Setting which knows change of the power system and acquires setting element using the Recursive Least Squares. Relay setting is very difficult work. But proposed relay is expected to decrease burden of relay setting using acquired information of power system change and autonomous setting. Also fast protective ability is expected according to change of power system.