• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1438-1446
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• 2019

Recently, the number of mobile users as well as high-speed Internet user has been increasing rapidly. Moreover, traffic is growing fast as services that provide real-time content such as Youtube and Netflix become popular. The problem of traffic control in real-time content services is important because many people use cell phones to receive real-time content. In this regard, the field of CCN is currently being studied. We studied the mobility of nodes among CCN research fields. Node mobility can be divided into three categories : consumer mobility, intermediate node, and provide mobility. In this paper, we propose Mobility Node Support(MD-INS) to support the intermediate-node mobility in CCN real-time services. Experimental results show that the proposed scheme shows better performance than CCN in terms of service disconnection time and packet loss.

• ETRI Journal
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• v.45 no.5
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• pp.847-861
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• 2023

Dynamic object detection is essential for ensuring safe and reliable autonomous driving. Recently, light detection and ranging (LiDAR)-based object detection has been introduced and shown excellent performance on various benchmarks. Although LiDAR sensors have excellent accuracy in estimating distance, they lack texture or color information and have a lower resolution than conventional cameras. In addition, performance degradation occurs when a LiDAR-based object detection model is applied to different driving environments or when sensors from different LiDAR manufacturers are utilized owing to the domain gap phenomenon. To address these issues, a sensor-fusion-based object detection and classification method is proposed. The proposed method operates in real time, making it suitable for integration into autonomous vehicles. It performs well on our custom dataset and on publicly available datasets, demonstrating its effectiveness in real-world road environments. In addition, we will make available a novel three-dimensional moving object detection dataset called ETRI 3D MOD.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.486-489
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• 2021

Recently, the demand for the use of personal mobility vehicles, such as an electric kickboard, is increasing explosively because of its high portability and usability. However, the number of traffic accidents caused by personal mobility vehicles has also increased rapidly in recent years. To address the issues regarding the driver's safety, we propose a novel approach that can monitor context information around personal mobility vehicles using deep learning-based object detection and smartphone captured videos. In the proposed framework, a smartphone is attached to a personal mobility device and a front or rear view is recorded to detect an approaching object that may affect the driver's safety. Through the detection results using YOLOv5 model, we report the preliminary results and validated the feasibility of the proposed approach.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1125-1136
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• 2010

Lately there exist increasing demands for online abnormality monitoring over trajectory stream, which are obtained from moving object tracking devices. This problem is challenging due to the requirement of high speed data processing within limited space cost. In this paper, we present a FADA (Fuzzy Anomaly Detection Algorithm) which constructs normal profile by computing mobility feature information from the GPS (Global Positioning System) logs of mobile devices in MANETs (Mobile Ad-hoc Networks), computes a fuzzy dissimilarity between the current mobility feature information of the mobile device and the mobility feature information in the normal profile, and detects effectively the anomaly behaviors of mobile devices on the basis of the computed fuzzy dissimilarity. The performance of proposed FADA is evaluated through simulation.

• Textile Coloration and Finishing
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• v.33 no.4
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• pp.288-301
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• 2021

The purpose of this study was to verify the degree of visibility of FOLED (fiber optic light-emitting diode) materials applied to safety-enhancing clothes of micro-mobility users during the day and night by conducting an empirical test targeting 50 people in their teens, 20's, 30's, 40's, and 50's or older. First, the results of the visibility test at 10 m-intervals from 10 to 70 m based on the clothes sample showed that the light detection of FOLED material was very good without daytime or night-time distinction. Second, the results of directional sign detection of FOLED were confirmed to be very high without any daytime or night. Third, the results of identifying a pictogram design showed that the distance was shorter than that of light detection or directional indication. However, the FOLED pictogram design could be confirmed at a distance of 50 m or less. Therefore, if a clothes product using FOLED material is worn and micro-mobility is used, the experimental results indicate that safety will be sufficiently secured due to the excellent visibility.

• ETRI Journal
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• v.45 no.4
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• pp.650-665
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• 2023

A novel smart metering technique capable of anomaly detection was proposed for real-time home power management system. Smart meter data generated in real-time were obtained from 900 households of single apartments. To detect outliers and missing values in smart meter data, a deep learning model, the autoencoder, consisting of a graph convolutional network and bidirectional long short-term memory network, was applied to the smart metering technique. Power management based on the smart metering technique was executed by multi-objective optimization in the presence of a battery storage system and an electric vehicle. The results of the power management employing the proposed smart metering technique indicate a reduction in electricity cost and amount of power supplied by the grid compared to the results of power management without anomaly detection.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.12
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• pp.301-312
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• 2018

In industrial wireless sensor networks, the continuous object detection such as fire or toxic gas detection is one of major applications. A continuous object occurs at a specific point and then diffuses over a wide area. Therefore, many studies have focused on accurately detecting a continuous object and delivering data to a static sink with an energy-efficient way. Recently, some applications such as fire suppression require mobile sinks to provide real-time response. However, the sink mobility support in continuous object detection brings challenging issues. The existing approaches supporting sink mobility are designed for individual object detection, so they establish one-to-one communication between a source and a mobile sink for location update. But these approaches are not appropriate for a continuous object detection since a mobile sink should establish one-to-many communication with all sources. The one-to-many communication increases energy consumption and thus shortens the network lifetime. In this paper, we propose the origin-centric communication scheme to support sink mobility in a continuous object detection. Simulation results verify that the proposed scheme surpasses all the other work in terms of energy consumption.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.588-591
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• 2004

This paper describes a new approach to Internet host mobility. We argue that local mobility, the performance of existing mobile host protocol can be significantly improved. It proposes Fast Moving Detection scheme that based on neighbor AP channel information and moving detection table. And, it composes Local Area Clustering Path (LACP) domain that collected in AP's channel information and MN interface information. It stored the roaming table to include channel information and moving detection. Those which use the proposal scheme will need to put LACP information into the beacon or probe frame. Each AP uses scheme to inform available channel information to MN. From the simulation result, we show that the proposed scheme is advantageous over the legacy schemes in terms of the burst blocking probability and the link utilization.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.57-65
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• 2022

International attempts to smuggle narcotics and explosives using ship or aircraft cargoes are on the rise. With the recent increase in the number of detection cases of narcotics and explosives in Korea, it is important to detect dangerous material (narcotics and explosives) through container searches at ports and airports, which are the main routes. This paper proposes a technique to detect dangerous material in cargo containers using the sampled output signal of ion mobility spectroscopy (IMS). The proposed technique estimates parameters such as a threshold, a window length, and a noise level for ion detection of the target dangerous material by using known materials in the initialization stage. The estimated parameters are used to detect the ions of the dangerous target material inside the containers. The proposed technique can be applied when the peak value of the IMS signal and the ion mobility are varying due to container environments.

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.387-399
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• 2009

Mobile wireless networks continue to be plagued by theft of identify and intrusion. Both problems can be addressed in two different ways, either by misuse detection or anomaly-based detection. In this paper, we propose a dissimilarity-based anomaly detection method which can effectively identify abnormal behavior such as mobility patterns of mobile wireless networks. In the proposed algorithm, a normal profile is constructed from normal mobility patterns of mobile nodes in mobile wireless networks. From the constructed normal profile, a dissimilarity is computed by a weighted dissimilarity measure. If the value of the weighted dissimilarity measure is greater than the dissimilarity threshold that is a system parameter, an alert message is occurred. The performance of the proposed method is evaluated through a simulation. From the result of the simulation, we know that the proposed method is superior to the performance of other anomaly detection methods using dissimilarity measures.