• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.415-426
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• 2021

Purpose: The purpose of this study is to create the deveopment plan for promotion of advanced disaster-safety awareness, which is noted as a major factor in the large disaster. Method: This study is to conduct theoretical review with regard to disaster management and safety awareness. Consciousness surveys on safety awareness and previous disaster case was analyzed to derive the cause of the disaster, and the development plan for promotion of advanced disaster-safety awareness was suggested. Result: In the survey on the public's sense of safety on the disaster management evaluation, 'Response' stage was well performed, but the 'Recovery' stage was not. Especially, it was found that disaster safety education at the 'Prevention' stage was very lacking. In the survey on the public's safety awareness, the awareness level of the evacuation facility was very low, information on infectious diseases and collapse accident was insufficient. Especially, it has been found that the awareness on safety regulation in daily life is very insufficient. Through the case study on previous disaster(COVID-19, Fire in Miryang Sejong Hospital, Forest fire in the east coas at 2004'), it was derived that the lack of safety awareness(such as safety insensitivity) was the main factor of the expansion of the damage scale. Conclusion: The development plan for promotion of advanced disaster-safety awareness are as follow. First, it is necessary to spread the safety culture movement through the expansion of safety education and safety promotion. Second, disaster confrontation training for the public should be implemented to improve the effectiveness of disaster response. Finally, it is necessary to change the individual awareness on safety. When these factors are implemented systematically, advanced disaster-safety awareness can be promoted. Ultimately, disaster accidents in our society can be reduced.

• Journal of IKEEE
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• v.23 no.4
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• pp.1461-1464
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• 2019

In this paper, we propose the development of embedded board for integrated radiation exposure protection fireman's life-saving alarm capable of location tracking and radiation measurement. The proposed techniques consist of signal processing unit, communication unit, power unit, main control unit. Signal processing units apply shielding design, noise reduction technology and electromagnetic wave subtraction technology. The communication unit is designed to communicate using the wifi method. In the main control unit, power consumption is reduced to a minimum, and a high performance system is formed through small, high density and low heat generation. The proposed techniques are equipment operated by exposure to poor conditions, such as disaster and fire sites, so they are designed and manufactured for external appearance considering waterproof and thermal endurance. The proposed techniques were tested by an authorized testing agency to determine the effectiveness of embedded board. The waterproof grade has achieved the IP67 rating, which can maintain stable performance even when flooded with water at the disaster site due to the nature of the fireman's equipment. The operating temperature was measured in the range of -10℃ to 50℃ to cope with a wide range of environmental changes at the disaster site. The battery life was measured to be available 144 hours after a single charge to cope with emergency disasters such as a collapse accident. The maximum communication distance, including the PCB, was measured to operate at 54.2 meters, a range wider than the existing 50 meters, at a straight line with the command-and-control vehicle in the event of a disaster. Therefore, the effectiveness of embedded board for embedded board for integrated radiation exposure protection fireman's life-saving alarm has been demonstrated.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.99-120
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• 2010

Due to the wide spread of customers' frequent access of non face-to-face services, there have been many attempts to improve customer satisfaction using huge amounts of data accumulated throughnon face-to-face channels. Usually, a call center is regarded to be one of the most representative non-faced channels. Therefore, it is important that a call center has enough agents to offer high level customer satisfaction. However, managing too many agents would increase the operational costs of a call center by increasing labor costs. Therefore, predicting and calculating the appropriate size of human resources of a call center is one of the most critical success factors of call center management. For this reason, most call centers are currently establishing a department of WFM(Work Force Management) to estimate the appropriate number of agents and to direct much effort to predict the volume of inbound calls. In real world applications, inbound call prediction is usually performed based on the intuition and experience of a domain expert. In other words, a domain expert usually predicts the volume of calls by calculating the average call of some periods and adjusting the average according tohis/her subjective estimation. However, this kind of approach has radical limitations in that the result of prediction might be strongly affected by the expert's personal experience and competence. It is often the case that a domain expert may predict inbound calls quite differently from anotherif the two experts have mutually different opinions on selecting influential variables and priorities among the variables. Moreover, it is almost impossible to logically clarify the process of expert's subjective prediction. Currently, to overcome the limitations of subjective call prediction, most call centers are adopting a WFMS(Workforce Management System) package in which expert's best practices are systemized. With WFMS, a user can predict the volume of calls by calculating the average call of each day of the week, excluding some eventful days. However, WFMS costs too much capital during the early stage of system establishment. Moreover, it is hard to reflect new information ontothe system when some factors affecting the amount of calls have been changed. In this paper, we attempt to devise a new model for predicting inbound calls that is not only based on theoretical background but also easily applicable to real world applications. Our model was mainly developed by the interactive decision tree technique, one of the most popular techniques in data mining. Therefore, we expect that our model can predict inbound calls automatically based on historical data, and it can utilize expert's domain knowledge during the process of tree construction. To analyze the accuracy of our model, we performed intensive experiments on a real case of one of the largest car insurance companies in Korea. In the case study, the prediction accuracy of the devised two models and traditional WFMS are analyzed with respect to the various error rates allowable. The experiments reveal that our data mining-based two models outperform WFMS in terms of predicting the amount of accident calls and fault calls in most experimental situations examined.