• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.5
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• pp.1201-1210
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• 2007

The urban streets as a featured and well performing public space for people should be organized in a way that can work as a whole system through network as well as that can provide amenities fur pedestrian. This is a study on Seattle's 'Blue Ring' project for an analysis focused on how its street weaving system organized and what are the design characteristics of the streets. The results are as followings. (1)Pedestrian oriented design, (2)Urban street as a part of open space that can accommodate human activities, (3)Integrated, not isolated with outer/ other open space, (4)Organically networked in a hierarchical manner that can promote pedestrian movement, (5)Utilization of regionally identical design elements. And Smart Growth concept lies in the core of 'Blue Ring' project.

• Journal of Korean Society of Transportation
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• v.5 no.2
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• pp.81-95
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• 1987

In line with the continued growth of car ownership, the traffic problems in central area of metropoles such as Seoul would become increasingly degraded. comparing with most western cities, the problems in Seoul are characterized by the improportionately high rates of intersection delay, station congestion, traffic accidents caused by weaving conflicts and pedestrian congestion. It is caused by the lack of flexibility I street network, which is prerequisite for upholding the efficacy of traffic management and control, resulted from the simplicity of network graph in terms of connectivity, street density and distribution by width. This pattern has been resulted from the prolonged policy pursuing the street-widening of the nagging bottleneck in such a short period since the 1950s, comparing that most western cities had undergone over several centuries an age of horse-and-vehicle transportation. In order to improve the expected traffic problems in central area over the coming periods of motorization, it is imperative to restructure the street network in Central Seoul so that the efficacy of traffic management and control may be operative. Based upon the long-range planning the street network should be restructured by stages so that cenral traffic may be controled by one-way operation and most through-traffic be detoured around fringe area.

• Journal of the Korean Institute of Landscape Architecture
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• v.35 no.3
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• pp.37-49
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• 2007

This study investigated how external environmentally-friendly factors adopted to improve the quality of outdoor spaces have been designed and used in the construction of apartment complexes. The goal of this study is to determine what areas should be improved through environmentally-friendly ways to create natural outdoor spaces in apartment complexes and enhance their availability and usefulness. For this study, 21 environmentally-friendly factors were chosen and a checklist by specific item was prepared. This study examine how these items were applied to basic planning, detail design, and construction in 4 target areas. As a result, it was found that the development of environmentally-friendly residential areas was an ultimate goal of the project during basic planning and design. All target areas focused on developing an inner greens network, eco-pond, brook, and pedestrian track as well as the growth of a variety of plants. Some differences have been observed, however, in terms of method. Furthermore, due to economic and technological problems and poor construction site conditions, green walls/green roofs/pedestrian tracks/use of rainwater have not been properly promoted. These kinds of problems must be improved through the development of environmentally-friendly construction methods and materials, the expansion of natural grounds areas, economic support, a satisfactory repair and maintenance system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3657-3682
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• 2018

This paper presents a Bluetooth and pedestrian dead reckoning (PDR)-based indoor fusion localization approach (BtPDR) using smartphones. A Bluetooth and PDR-based indoor fusion localization approach can localize the initial position of a smartphone with the received signal strength (RSS) of Bluetooth. While a smartphone is moving, BtPDR can track its position by fusing the localization results of PDR and Bluetooth RSS. In addition, BtPDR can adaptively modify the parameters of PDR. The contributions of BtPDR include: a Bluetooth RSS-based Probabilistic Voting (BRPV) localization mechanism, a probabilistic voting-based Bluetooth RSS and PDR fusion method, and a heuristic search approach for reducing the complexity of BRPV. The experiment results in a real scene show that the average positioning error is < 2m, which is considered adequate for indoor location-based service applications. Moreover, compared to the traditional PDR method, BtPDR improves the location accuracy by 42.6%, on average. Compared to state-of-the-art Wireless Local Area Network (WLAN) fingerprint + PDR-based fusion indoor localization approaches, BtPDR has better positioning accuracy and does not need the same offline workload as a fingerprint algorithm.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1457-1465
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• 2019

3D convolution is to stack multiple consecutive frames to form a cube, and then apply the 3D convolution kernel in the cube. In this structure, each feature map of the convolutional layer is connected to multiple adjacent sequential frames in the previous layer, thus capturing the motion information. However, due to the changes of pedestrian posture, motion and position, the convolution at the same place is inappropriate, and when the 3D convolution kernel is convoluted in the time domain, only time domain features of three consecutive frames can be extracted, which is not a good enough to get action information. This paper proposes an action recognition method based on feature fusion of 3D convolutional neural network. Based on the VGG16 network model, sending a pre-acquired optical flow image for learning, then get the time domain features, and then the feature of the time domain is extracted from the features extracted by the 3D convolutional neural network. Finally, the behavior classification is done by the SVM classifier.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.126-129
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• 2015

Device-to-device (D2D) communications, a subset of Proximity-based Services that enables direct communication between LTE network subscribers, is gaining popularity. It is well underway to be adopted in cellular communication systems for pedestrian and connected-vehicles alike. In this paper, we briefly present our model of an Evolved Packet Core Network-assisted device discovery simulator and show the applicability of Proximity-based Services for Vehicular Ad-hoc Networks. Through the performance evaluation based on the developed simulation environment, it is shown that in case when users gather in the same vicinity, as in public transportation, LTE network data can be efficiently offloaded and multicasted through Wi-Fi for e.g. delivering traffic-related information and for the benefit of infotainment service consumers.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.115-123
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• 2016

The road name address system has been in effect in Korea since 2012. However, the existing address system is still being used in many fields because of the difference between the spatial awareness of people and the road name address system. For the spatial awareness based on the road name address system, various spatial datasets in daily life should be referenced by the road names. The goal of this paper is to link the road name address system with the wayfinding process, which is closely related to the spatial awareness. To achieve our goal, we designed and implemented a geometric matching method for spatial data sets. This method generates network neighborhoods from road objects in the 'road name address map' and the 'pedestrian network data'. Then it computes the geometric similarities between the neighborhoods to identify corresponding road name for each object in the network data. The performance by F0.5 was assessed at 0.936 and it was improved to 0.978 by the manual check for 10% of the test data selected by the similarity. By help of our method, the road name address system can be utilized in the wayfinding services, and further in the spatial awareness of people.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.11-19
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• 2021

An abnormal object refers to a person, an object, or a mechanical device that performs abnormal and unusual behavior and needs observation or supervision. In order to detect this through artificial intelligence algorithm without continuous human intervention, a method of observing the specificity of temporal features using optical flow technique is widely used. In this study, an abnormal situation is identified by learning an algorithm that translates an input image frame to an optical flow image using a Generative Adversarial Network (GAN). In particular, we propose a technique that improves the pre-processing process to exclude unnecessary outliers and the post-processing process to increase the accuracy of identification in the test dataset after learning to improve the performance of the model's abnormal behavior identification. UCSD Pedestrian and UMN Unusual Crowd Activity were used as training datasets to detect abnormal behavior. For the proposed method, the frame-level AUC 0.9450 and EER 0.1317 were shown in the UCSD Ped2 dataset, which shows performance improvement compared to the models in the previous studies.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.9
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• pp.923-928
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• 2016

The various technologies related to the monitoring human health in real-time for the emergency situations are developing these days. Mostly the human pulse is used for measuring as the vital signs so far, but the EEG became a major research trend now. However, there are some problems measuring and sending EEG signals of all the people walking down the street to the dedicated server. Especially, there are some restrictions for collecting and sending EEG signals in 2-dimensional space in real-time. Therefore, I suggests an efficient network model using 3-dimensional space of drones to avoid the restrictions. The models are designed, simulated, and evaluated with the Opnet simulator.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2170-2190
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• 2012

There are nowadays strong demands for intelligent surveillance systems, which can infer or understand more complex behavior. The application of crowd density estimation methods could lead to a better understanding of crowd behavior, improved design of the built environment, and increased pedestrian safety. In this paper, we propose a new crowd density estimation method, which aims at estimating not only a moving crowd, but also a stationary crowd, using images captured from surveillance cameras situated in various public locations. The crowd density of the moving people is measured, based on the moving area during a specified time period. The moving area is defined as the area where the magnitude of the accumulated optical flow exceeds a predefined threshold. In contrast, the stationary crowd density is estimated from the coarseness of textures, under the assumption that each person can be regarded as a textural unit. A multilayer neural network is designed, to classify crowd density levels into 5 classes. Finally, the proposed method is experimented with PETS 2009 and the platform of Gangnam subway station image sequences.