• The Journal of the Korea Contents Association
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• v.20 no.4
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• pp.396-405
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• 2020

In this study, we used the big data of Voice of Customer (VOC) related to high-speed Internet products to look at the causes of perceived quality and the possibility of proactive service. In order to verify the possibility of proactive service, we collected VOC data from 13 facilities and equipment of a mobile communication service company, and then conducted 𝒙² test to verify that there was a statistically significant difference between the actual VOC observation values and expected values. We found statistical evidence that proactive service is possible through real-time monitoring for the six disability alarms among the 13 facilities and equipment, which are FTTH-R Equipment ON/OFF, FTTH-EV Line Error Detection, Port Faulty, FTTH-R Line Error Detection, Network Loop Detection, and Abnormal Limiting Traffic. Companies are able to adopt the proactive service to improve their market share and to reduce customer service costs. The results of this study are expected to contribute to the actual application of industry in that it has diagnosed the possibility of proactive service in the telecommunication service sector and further suggested suggestions on how to provide effective proactive service.

• Journal of Wetlands Research
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• v.15 no.4
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• pp.529-534
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• 2013

Domestic water pollution accidents and response management were analysed on the basis of collected data from the latest 5 years. Although average 66.7 number of accidents were happened every year, no damages of human life were reported yet. According to the data collected, the accidents were occurred at Han river, Nakdong river, Keum river, Youngsang river and other rivers, where the percentages were 25.4%, 20.3%, 12%, 8% and 29.7%, respectively. Main reasons were blamed for negligent management, mixed influences, natural phenomenon and traffic accident. Response activities were performed in the case of the oil leak, the fish death caused by water environment, the spill of chemicals. From the diagnosis of water pollution accidents, it is recommended that the legistration of all control centers for their roles and duties was made in case of the big accidents as well as the small/middle accidents.

• The Journal of the Convergence on Culture Technology
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• v.6 no.2
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• pp.437-447
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• 2020

The purpose of this study is to research the countermeasures and expected effects through the use of drones in the field of disaster prevention as a drone-based smart quarantine performance method. The environmental, market, and technological approaches to the review of the current quarantine performance task and its countermeasures are as follows. First, in terms of the environment, the effectiveness of the quarantine performance business using drone-based control is to broaden the utilization of forest, bird flu, livestock, facility areas, mosquito larvae, pests, and to simplify and provide various effective prevention systems such as AI and cholera. Second, in terms of market, the standardization of livestock and livestock quarantine laws and regulations according to the use of disinfection and quarantine missions using domestic standardized drones through the introduction of new technologies in the quarantine method, shared growth of related industries and discovery of new markets, and animal disease prevention It brings about the effect of annual budget savings. Third, the technical aspects are (1) on-site application of disinfection and prevention using multi-drone, a new form of animal disease prevention, (2) innovation in the drone industry software field, and (3) diversification of the industry with an integrated drone control / control system applicable to various markets. (4) Big data drone moving path 3D spatial information analysis precise drone traffic information ensures high flight safety, (5) Multiple drones can simultaneously auto-operate and fly, enabling low-cost, high-efficiency system deployment, (6) High precision that this was considered due to the increase in drone users by sector due to the necessity of airplane technology. This study was prepared based on literature surveys and expert opinions, and the future research field needs to prove its effectiveness based on empirical data on drone-based services. The expected effect of this study is to contribute to the active use of drones for disaster prevention work and to establish policies related to them.

• The Journal of the Korea Contents Association
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• v.19 no.10
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• pp.685-697
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• 2019

The study aimed to analyze the structure of China's passenger railway network in the years of 2008, 2013, and 2018, respectively. At the same time, it tried to investigate its derivative impact on the patterns of Chinese urban network. The analytical tool was based on the NetMiner4.0. In order to measure network characteristics of China's passenger railway network, it primarily focused on the degree centrality, betweenness centrality, and closeness centrality. First of all, the higher degree centralities, with a few exceptions, were observed in BeiJing, ShangHai, GuangZhou, WuHan, XiAn, ChengDu, HaErBin, and ShenYang over a decade. In contrast, the higher betweenness centralities were recorded in cities of higher development potential including WuLuMuQi, GuiYang, ShenYang, and KunMing. The closeness centrality analyses confirmed the fact that most metropoles like BeiJing, ShangHai, and GuangZhou kept the highest train accessibility during the same research period. At the same time, the opening up of a new stretch of high speed railway network has consecutively strengthened connectivity between BeiJing and TianJin. Owing to unprecedented development of railway traffic and its extensive operations, this study believes that Chinese major cities, without interruption, would pursue a series of urban policy alternatives geared towards railway stations-oriented networking and competitively try to extend their network ranges.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.562-565
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. However, this irregular weight distribution is not possible to be recognized with the current weight measurement system for vehicles on roads. To address this limitation, we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles from the CCTV, black box, and hand-held camera point of view. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data. From the result, we believe that public big data can be utilized more efficiently and applied to the development of an object detection-based overloaded vehicle detection model.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.6
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• pp.7-20
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• 2017

The advancement of cloud and big data and the considerable growth of traffic have increased the complexity and problems in the management inefficiency of existing networks. The software-defined networking (SDN) environment has been developed to solve this problem. SDN enables us to control network equipment through programming by separating the transmission and control functions of the equipment. Accordingly, several studies have been conducted to improve the performance of SDN controllers, such as the method of connecting existing legacy equipment with SDN, the packet management method for efficient data communication, and the method of distributing controller load in a centralized architecture. However, there is insufficient research on the control of SDN in terms of the quality of network-using applications. To support the establishment and change of the routing paths that meet the required network service quality, we require a mechanism to identify network requirements based on a contract for application network service quality and to collect information about the current network status and identify the violations of network service quality. This study proposes a method of identifying the quality violations of network paths through ontology to ensure the network service quality of applications and provide efficient services in an SDN environment.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.1
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• pp.95-107
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• 2017

In this study, a preliminary study was undertaken for development of a tunnel incident automatic detection system based on a machine learning algorithm which is to detect a number of incidents taking place in tunnel in real time and also to be able to identify the type of incident. Two road sites where CCTVs are operating have been selected and a part of CCTV images are treated to produce sets of training data. The data sets are composed of position and time information of moving objects on CCTV screen which are extracted by initially detecting and tracking of incoming objects into CCTV screen by using a conventional image processing technique available in this study. And the data sets are matched with 6 categories of events such as lane change, stoping, etc which are also involved in the training data sets. The training data are learnt by a resilience neural network where two hidden layers are applied and 9 architectural models are set up for parametric studies, from which the architectural model, 300(first hidden layer)-150(second hidden layer) is found to be optimum in highest accuracy with respect to training data as well as testing data not used for training. From this study, it was shown that the highly variable and complex traffic and incident features could be well identified without any definition of feature regulation by using a concept of machine learning. In addition, detection capability and accuracy of the machine learning based system will be automatically enhanced as much as big data of CCTV images in tunnel becomes rich.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37C no.9
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• pp.829-840
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• 2012

Recently, in network communication environments, it is changing very fast from wired to wireless. The open shortest path firtst (OSPF), one of link state routing protocols, mainly used in wired networks, is the routing method to select optimal traffic path as identifying the link state of neighbor routers. The traditional OSPF cost functions performs with first fixed cost permanently, unless the router link is changed. However, in wireless networks, the performance of links show big difference by other environment factors. The bit error rate (BER), a parameter which can quite affect link state in wireless networks, is not considered in the traditional OSPF cost functions. Only a link bandwidth is considered in the traditional OSPF cost functions. In this paper, we verify the various parameters which can affect link performance, whether it is permissible to use as the parameter of proposed cost functions. To propose new cost functions, we use the effective bandwidth. This bandwidth is calculated by proposed formula using the BER of the network link and link bandwidth. As applied by the proposed triggering condition, the calculated effective bandwidth decrease the unstable of network by generating less link state update messages in wireless networks that frequently changes the link state. Simulation results show that the proposed cost functions significantly outperforms the traditional cost functions in wireless networks in terms of the services of VoIP and data transmission.

• Journal of The Korean Association of Information Education
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• v.24 no.1
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• pp.21-28
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• 2020

Technological advancements such as artificial intelligence, robots, and big data are revolutionizing the entire society. In this paper, we analyzed preliminary teachers' daily internet/gaming/smartphone habit and the difference between preference factors in gender and diagnosis group in the situation of ethical dilemma in driverless cars. The result shows most of the male students are in high risk group of daily internet/gaming usage, and male students tend to be more immersed in games compared to female students, which negatively affects their daily lives. Students who have at least one of the daily internet/gaming/smartphone habits are more likely to be classified as high-risk group in all three of daily internet/gaming/smartphone habit. Fortunately, the students perceived themselves addicted and wanted change their habits. An analysis by a moral machine of these students tells that there is no significant difference in preference between male and female students and among diagnosis groups. However, specifically in the ethical dilemma of driverless cars, all the groups of male, female, normal, high-risk showed they have priority in pedestrians over drivers, a large number of people over small, and people who obey traffic rules over who do not. The tendency was pronounced in female group and high-risk students prioritized people who are older and in lower social status.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.6
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• pp.69-76
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• 2015

Although traffic information has been actively analyzed using big data, it has not been used as much with the consideration of driver characteristics. Among the various types of information, road signs can directly affect the driver. Road signs must provide the optimal information that enables drivers to reach their destinations with ease as well as information suitable for navigation systems. However, present road sign rules provide standardized information, regardless of the road type or size. This study suggests a method for providing road information that will help drivers determine their behavior. First, the minimum character size that can be used on a road sign for each design speed was obtained with respect to the visibility and decipherability of a road sign. Instead of conventional diagram-based direction guidance, a scenario using split-based direction guidance was created. To verify the effectiveness of the provided information, a three-dimensional simulated road environment was constructed, and a driving simulator was used for the test. At a simple plane intersection, the driver was not greatly influenced by directional guidance, but at a complex, three-dimensional intersection, the driver preferred summary-based directional guidance, which is instinctive guidance, over diagram-based guidance. On the basis of the test results, a secondary verification test that applied split-based guidance at a three-dimensional intersection confirmed that the driver had no problems in making decisions.