• Journal of the Korean Geotechnical Society
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• v.29 no.12
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• pp.87-94
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• 2013

In recent years, tunnel construction is being increased in order to resolve traffic congestion around urban area, however there are a lot of difficulties due to restrictions such as interference with existing alignment, adjacent structures and cost increase of land acquisition as well as public complaints for negative environmental impacts near the expected tunnel construction site. Therefore, applications of close parallel tunnel have been increasing greatly. But close parallel tunnels cannot guarantee the stability compared with normal parallel tunnel which has enough distance between tunnels. So various methods to strengthen the pillar have been introduced recently, however there is few methods which consider the pillar behaviour in the state of compression. In this paper, the reinforcement methods which reflect the behavior of pillar were reviewed with comparision and analysis by numerical method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.121-126
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• 2018

IEEE 802.1 TSN TG is developing standards for end-to-end delay bounds and zero packet loss based on Ethernet technology. We focus on packet forwarding techniques. TSN packet forwarding techniques can be classified into Synchronous and Asynchronous framework. Synchronous approach allocates fixed time period for a class, yet is complex for large networks. Asynchronous approach provides delay guarantee by regulator-scheduler pair, yet is unnecessarily complex, too. We propose network components for TSN Asynchronous architecture, which remove the complexity of maintaining flow state for regulation decisions. Despite such a simplicity, the proposed architecture satisfies the TSN's delay requirements provided the limited high priority traffic's maximum packet length.

• Journal of Advanced Navigation Technology
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• v.23 no.2
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• pp.114-124
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• 2019

The drone market, an unmanned aerial vehicle, is rapidly expanding and developing into an important area related to the huge changes in the traffic system of the future. With various technologies on the fourth industrial revolution, including drones, mentioned at the Davos Forum in January 2016, interest in drones is emerging as an explosive demand for national certificates. The number of drone pilots, which was only 400 in 2015, is continuing to surpass 17,000 as of 2018. Therefore, this study analyzed the safety perception of the pilots based on the DREEM (Dundee ready environmental assessment) model designed to evaluate the educational environment along with the current state of drone education in Korea. This led to the conclusion that the high level of satisfaction of the pilot with the educational environment contributes to the overall safety perception, including compliance with procedures.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.63-74
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• 2021

Location-based services (LBSs) are expected to process a large number of spatial queries, such as shortest path and k-nearest neighbor queries that arrive simultaneously at peak periods. Deploying more LBS servers to process these simultaneous spatial queries is a potential solution. However, this significantly increases service operating costs. Recently, batch processing solutions have been proposed to process a set of queries using shareable computation. In this study, we investigate the problem of batch processing moving k-nearest neighbor (MkNN) queries in dynamic spatial networks, where the travel time of each road segment changes frequently based on the traffic conditions. LBS servers based on one-query-at-a-time processing often fail to process simultaneous MkNN queries because of the significant number of redundant computations. We aim to improve the efficiency algorithmically by processing MkNN queries in batches and reusing sharable computations. Extensive evaluation using real-world roadmaps shows the superiority of our solution compared with state-of-the-art methods.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.33 no.12
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• pp.105-112
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• 2017

Through various media, damage of both smoking and second-hand smoking has been recognized, and brought global scale of interest in antismoking. In Korea, government has tightened regulations of smoking in non-smoking zone since December, 1980, and after National Health Promotion Act in 1995, non-smoking zone has been gradually expanded. On the other hand, there were law suits to find those regulation towards smokers are either unconstitutional or not for 4 times. In this current state, people need smoking area to prevent second-hand smoking and to consider smokers in multi-unit dwelling. Main purpose of this research is to plan smoking spaces based on various typology of multi-dwelling plan for protection of both smokers and non-smokers' right. The research group collected and analyzed the smoking behaviors in various multi-unit dwelling types such as flat type, tower type, hybrid type and others. Based on those data, the group found three phenomena. First, there are internal regulations in multi-unit dwelling to make non-smoking zone based on National Health Promotion Act and resident representative meeting decision. Second, main smoking activities are occurring at major traffic line and entrances. Third, smoking inside of multi-unit dwelling complex causes second-hand smoking to residents live in $1^{st}$ floor and when they enter. Therefore, one can achieve both smokers' and non-smokers' protection of right by creating a designated smoking space near main entrances of multi-unit dwelling complex to consider smokers' and prevents second-hand smoking by using shaft space, which is in core space, to ventilate tobacco smoke through roof.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.3
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• pp.545-552
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• 2012

The rapid development of information technology is making large changes in our lives today. Also the infrastructure and services are combinding with information technology which predicts another huge change in our environment. However, the development of information technology brings various types of side effects and these side effects not only cause financial loss but also can develop into a nationwide crisis. Therefore, the detection and quick reaction towards these side effects is critical and much research is being done. Intrusion detection systems can be an example of such research. However, intrusion detection systems mostly tend to focus on judging whether particular traffic or files are malicious or not. Also it is difficult for intrusion detection systems to detect newly developed malicious codes. Therefore, this paper proposes a method which determines whether the present network model is normal or abnormal by comparing it with past network situations.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1099-1104
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• 2020

In order to provide various types of application services based on the Internet, it is ideal to guarantee the quality of service(QoS) for each flow. However, realizing these ideas is not an easy task.. It is effective to classify multiple flows having the same or similar service quality requirements into same group, and to provide service quality for each group. The queue management mechanism in the router plays a very important role in order to efficiently transmit data and to support differentiated quality of service for each service. In order to efficiently support various multimedia services, an intelligent and adaptive queue management mechanism is required. This paper proposes an intelligent queue management mechanism based on deep reinforcement learning that decides whether to deliver packets for each group based on the traffic information of each flow group flowing in for a certain period of time and the current network state information.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.859-864
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• 2022

Drowsy driving, one of the biggest causes of traffic accidents every year, is accompanied by various factors. As a general method to check whether or not there is drowsiness, a method of identifying a driver's expression and driving pattern, and a method of analyzing bio-signals are being studied. This paper proposes a driver fatigue detection system using deep learning technology and bio-signal measurement technology. As the first step in the proposed method, deep learning is used to detect the driver's eye shape, yawning presence, and body movement to detect drowsiness. In the second stage, it was designed to increase the accuracy of the system by identifying the driver's fatigue state using the pulse wave signal and body temperature. As a result of the experiment, it was possible to reliably determine the driver's drowsiness and fatigue in real-time images.

• Steel and Composite Structures
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• v.41 no.6
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• pp.831-850
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• 2021

Surface subsidence caused by mining subsidence has an impact on neighboring structures and utilities. In other words, subsurface voids created by mining or tunneling activities induce soil movement, exposing buildings to physical and/or functional destruction. Soil-structure is evaluated employing probability distribution laws to account for their uncertainty and complexity to estimate structural vulnerability. In this study, to investigate the displacement field and surface settlement profile caused by mining subsidence, on the basis of a Winklersoil model, analytical equations for the moment-rotation response ofsoil during mining induced ground movements are developed. To define the full static moment-rotation response, an equation for the uplift-yield state is constructed and integrated with equations for the uplift- and yield-only conditions. The constructed model's findings reveal that the inverse of the factor of safety (x) has a considerable influence on the moment-rotation curve. The maximal moment-rotation response of the footing is defined by X = 0:6. Despite the use of Winkler model, the computed moment-rotation response results derived from the literature were analyzed through the ELM-SVM hybrid of Extreme Learning Machine (ELM) and Support Vector Machine (SVM). Also, Monte Carlo simulations are used to apply continuous random parameters to assess the transmission of ground motions to structures. Following the findings of RMSE and R2, the results show that the choice of probabilistic laws of input parameters has a substantial impact on the outcome of analysis performed.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.39-47
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• 2023

The development of autonomous driving and Advanced Driver Assistance System (ADAS) technology has grown rapidly in recent years. As most traffic accidents occur due to human error, self-driving vehicles can drastically reduce the number of accidents and crashes that occur on the roads today. Obviously, technical advancements in autonomous driving can lead to improved public driving safety. However, due to the current limitations in technology and lack of public trust in self-driving cars (and drones), the actual use of Autonomous Vehicles (AVs) is still significantly low. According to prior studies, people's acceptance of an AV is mainly determined by trust. It is proven that people still feel much more comfortable in personalized ADAS, designed with the way people drive. Based on such needs, a new attempt for a customized ADAS considering each driver's driving style is proposed in this paper. Each driver's behavior is divided into two categories: assertive and defensive. In this paper, a novel customized ADAS algorithm with high classification accuracy is designed, which divides each driver based on their driving style. Each driver's driving data is collected and simulated using CARLA, which is an open-source autonomous driving simulator. In addition, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) machine learning algorithms are used to optimize the ADAS parameters. The proposed scheme results in a high classification accuracy of time series driving data. Furthermore, among the vast amount of CARLA-based feature data extracted from the drivers, distinguishable driving features are collected selectively using Support Vector Machine (SVM) technology by comparing the amount of influence on the classification of the two categories. Therefore, by extracting distinguishable features and eliminating outliers using SVM, the classification accuracy is significantly improved. Based on this classification, the ADAS sensors can be made more sensitive for the case of assertive drivers, enabling more advanced driving safety support. The proposed technology of this paper is especially important because currently, the state-of-the-art level of autonomous driving is at level 3 (based on the SAE International driving automation standards), which requires advanced functions that can assist drivers using ADAS technology.