• Journal of the Korean Institute of Gas
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• v.25 no.3
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• pp.46-52
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• 2021

In this study, the on-site location identification and response system was proposed by accurately checking the location information of rescue requesters in the buildings using the smartphone Wi-Fi AP. The location server was requested to measure the strength of the Wi-Fi AP at least 25 times at 8 different building location points. And the accuracy of the position and the error range were checked by analyzing the coordinate values of the received positions. In addition, the response time was measured by changing the conditions of location information in three groups to compare the response time for saving lives with and without location information. The minimum and maximum error values for the eight cases were found to be at least 4.137 m and up to 14.037 m, respectively, with an average error of 9.525 m. Compared to the base transceiver station (BTS) based position error value of 263m, the range could be reduced by up to 93%. When the location information was given, it took 10 minutes and 50 seconds to save lives; however, when there was no location information at all, rescue process took more than 45 minutes. From this research effort, it was analyzed that the acquisition of the location information of rescuees in the building using the smartphone Wi-Fi AP approach is effective in reducing the life-saving time for on-site responses.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.624-629
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• 2018

The current study was conducted to develop a portable composite gas detector allowing the detection of both $CO_2$ and $CH_4$ gases by means of the Non Dispersive Infra-Red (NDIR) method. The gas detector is configured to radiate infrared waves using infrared lamps, where the wavelength of the infrared light is reduced due to absorption throughout the chamber, and this reduction (absorption) is detected by the absorption detector, before being converted and amplified to a 3.5V~6V electrical signal, providing as accurate a measurement as possible. The conventional singe sensor method measures the relative measurement by absorbing only specified wavelengths of infrared radiation, which in the case of gas detection leads to problems with accuracy due to the lack of a reference sensor when detecting light with a wavelength of only $4.26{\mu}m$. The dual sensor employed in this study provides a comparative measurement between the reference value derived from the wavelength of $3.91{\mu}m$, which is not influenced by other gas sources, and the measurement value derived from the wavelength of $4.26{\mu}m$, in order to reduce the errors and enhance the reliability, thereby allowing low power consumption for portable devices and multi-gas detection for both $CO_2$ and $CH_4$ gases. The portable composite gas detector developed herein provides a measurement rage of 0ppm~5,000ppm for $CO_2$ gas, and 0.5%vol for $CH_4$, which allows the determination of whether the $CO_2$ and $CH_4$ contents in indoor air are less than 1,000ppm or not. The current study established that the composite gas detector can be interlinked with firefighting appliances through portable devices or home automation, and is anticipated to be very effective in fire prevention.

• Journal of the Korean Geotechnical Society
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• v.23 no.4
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• pp.15-24
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• 2007

Two kinds of temperature monitoring technology have been introduced in this study, which can measure coincidently temperatures at many points along a single length of cable. One is to use a thermal sensor cable comprizing of addressable thermal sensors. The other is to use an optic fiber sensor with Distributed Temperature Sensing (DTS) system. The differences between two technologies can be summarized as follows: A thermal sensor cable has a concept of "point sensing" that can measure temperature only at a predefined position. The accuracy and resolution of temperature measurement are up to the capability of the individual thermal sensor. On the other hand, an optic fiber sensor has a concept of "distributed sensing" because temperature is measured practically at all points along the fiber optic cable by analysing the intensity of Raman back-scattering when a laser pulse travels along the fiber. Thus, the temperature resolution depends on the measuring distance, measuring time and spatial resolution. The purpose of this study is to investigate the applicability of two different temperature monitoring techniques in technical and economical sense. To this end, diverse experiments with two techniques were performed and two techniques are applied under the same condition. Considering the results, the thermal sensor cable will be well applicable to the assessment of groundwater flow, geothermal distribution and grouting efficiency within about loom distance, and the optic fiber sensor will be suitable for long distance such as pipe line inspection, tunnel fire detection and power line monitoring etc.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.2
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• pp.135-146
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• 2020

To define an interface in a conventional level-set method, an analytical function must be revealed for an interfacial geometry. However, it is not always possible to define a functional form of level sets when interfaces become complex in a Cartesian coordinate system. To overcome this difficulty, we have developed a new level-set formalism that discriminates the interior from the exterior of a CAD modeled interface by parameterizing the stereolithography (STL) file format. The work outlined here confirms the accuracy and scalability of the hydrodynamic reactive solver that utilizes the new level set concept through a series of tests. In particular, the complex interaction between shock and geometrical confinements towards deflagration-to-detonation transition is numerically investigated. Also, a process of flame spreading and damages caused by point source detonation in a real-sized plant facility have been simulated to confirm the validity of the new method built for reactive hydrodynamic simulation in any complex three-dimensional geometries.

• Journal of Service Research and Studies
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• v.13 no.1
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• pp.127-143
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• 2023

Currently, the domestic traditional market has not escaped the swamp of stagnation that began in the early 2000s despite various projects promoted by many related players such as the central government and local governments. In order to overcome the crisis faced by the traditional market, various R&Ds have recently been conducted on how to build a smart traditional market that combines information and communication technologies such as big data analysis, artificial intelligence, and the Internet of Things. This study analyzes various previous studies, users of traditional markets, and application cases of ICT technology in foreign traditional markets since 2012 and proposes a model to build a smart traditional market using ICT technology based on the analysis. The model proposed in this study includes building a traditional market metaverse that can interact with visitors, certifying visits to traditional markets through digital signage with NFC technology, improving accuracy of fire detection functions using IoT and AI technology, developing smartphone apps for market launch information and event notification, and an e-commerce system. If a smart traditional market platform is implemented and operated based on the smart traditional market platform model presented in this study, it will not only draw interest in the traditional market to MZ generation and foreigners, but also contribute to revitalizing the traditional market in the future.

• 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.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.465-486
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• 2021

Purpose: Since most disaster information systems are centered on non-disabled people, the reality is that there is a lack of disaster information delivery systems for the vulnerable, such as the disabled, the elderly, and children, who are relatively vulnerable to disasters. The purpose of the service is to improve the safety of the disabled and the elderly by eliminating blind spots of informatization and establishing customized disaster information services to respond to disasters through IoT-based integrated control technology. Method: The model at the core of this study is the disaster alert propagation model and evacuation support model, and it shall be developed by reflecting the behavioral characteristics of the disabled and the elderly in the event of a disaster. The disaster alert propagation model spreads disaster situations collected using IoT technology, and the evacuation support model uses geomagnetic field-based measuring technology to identify the user's indoor location and help the disabled and the elderly evacuate safely. Results: Demonstration model demonstration resulted in an efficient qualitative evaluation of indoor location accuracy, such as the suitability of evacuation route guidance and satisfaction of services from the user's perspective. Conclusion: Disaster information and evacuation support services were established for the safety vulnerable groups of mobile app for model verification. The disaster situation was demonstrated through experts in the related fields and the disabled by limiting it to the fire situation. It was evaluated as "satisfaction" in the adequacy of disaster information delivery and evacuation support, and its functional satisfaction and user UI were evaluated as "normal" due to the nature of the pilot model. Through this, the disaster information and evacuation support services presented in this study were evaluated to support the safety vulnerable groups to a faster disaster evacuation without missing the golden time of disaster evacuation.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.99-107
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• 2024

Along with economic growth and industrial development, there is an increasing demand for various electronic components and device production of semiconductor, SMT component, and electrical battery products. However, these products may contain foreign substances coming from manufacturing process such as iron, aluminum, plastic and so on, which could lead to serious problems or malfunctioning of the product, and fire on the electric vehicle. To solve these problems, it is necessary to determine whether there are foreign materials inside the product, and may tests have been done by means of non-destructive testing methodology such as ultrasound ot X-ray. Nevertheless, there are technical challenges and limitation in acquiring X-ray images and determining the presence of foreign materials. In particular Small-sized or low-density foreign materials may not be visible even when X-ray equipment is used, and noise can also make it difficult to detect foreign objects. Moreover, in order to meet the manufacturing speed requirement, the x-ray acquisition time should be reduced, which can result in the very low signal- to-noise ratio(SNR) lowering the foreign material detection accuracy. Therefore, in this paper, we propose a five-step approach to overcome the limitations of low resolution, which make it challenging to detect foreign substances. Firstly, global contrast of X-ray images are increased through histogram stretching methodology. Second, to strengthen the high frequency signal and local contrast, we applied local contrast enhancement technique. Third, to improve the edge clearness, Unsharp masking is applied to enhance edges, making objects more visible. Forth, the super-resolution method of the Residual Dense Block (RDB) is used for noise reduction and image enhancement. Last, the Yolov5 algorithm is employed to train and detect foreign objects after learning. Using the proposed method in this study, experimental results show an improvement of more than 10% in performance metrics such as precision compared to low-density images.