• Journal of Positioning, Navigation, and Timing
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• v.12 no.2
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• pp.91-100
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• 2023

The existing indoor localization method using Wi-Fi fingerprinting has a high collection cost and relatively low accuracy, thus requiring integrated correction of convergence with other technologies. This paper proposes a new method that significantly reduces collection costs compared to existing methods using Wi-Fi fingerprinting. Furthermore, it does not require labeling of data at collection and can estimate pedestrian travel paths even in large indoor spaces. The proposed pedestrian movement path estimation process is as follows. Data collection is accomplished by setting up a feature area near an indoor space intersection, moving through the set feature areas, and then collecting data without labels. The collected data are processed using Kernel Linear Discriminant Analysis (KLDA) and the valley point of the Euclidean distance value between two data is obtained within the feature space of the data. We build learning data by labeling data corresponding to valley points and some nearby data by feature area numbers, and labeling data between valley points and other valley points as path data between each corresponding feature area. Finally, for testing, data are collected randomly through indoor space, KLDA is applied as previous data to build test data, the K-Nearest Neighbor (K-NN) algorithm is applied, and the path of movement of test data is estimated by applying a correction algorithm to estimate only routes that can be reached from the most recently estimated location. The estimation results verified the accuracy by comparing the true paths in indoor space with those estimated by the proposed method and achieved approximately 90.8% and 81.4% accuracy in two experimental spaces, respectively.

• Journal of Digital Contents Society
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• v.18 no.1
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• pp.109-114
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• 2017

In our modern society, the utilization of the advanced drone which is capable of performing variety of tasks has been gradually increasing. In this paper, we present an application, similar to the prototype "Skycall" that had been introduced in the MIT Senseable City. To assess this concept, we have implemented a prototype of drone-based pedestrian navigation depending on the Android smartphone. Our system is not only able to guide the user in a very complicated place, where buildings are compacted, but also to block unauthorized visitors from accessing the facilities. And we discuss some problems we found and suggest the direction to address them.

• Journal of Internet of Things and Convergence
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• v.10 no.4
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• pp.101-108
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• 2024

Indoor localization is a critical component for numerous applications, ranging from navigation in large buildings to emergency response. This paper presents an enhanced Pedestrian Dead Reckoning (PDR) scheme using smartphone sensors, integrating neural network-aided motion recognition, Kalman filter-based error correction, and multi-sensor data fusion. The proposed system leverages data from the accelerometer, magnetometer, gyroscope, and barometer to accurately estimate a user's position and orientation. A neural network processes sensor data to classify motion modes and provide real-time adjustments to stride length and heading calculations. The Kalman filter further refines these estimates, reducing cumulative errors and drift. Experimental results, collected using a smartphone across various floors of University, demonstrate the scheme's ability to accurately track vertical movements and changes in heading direction. Comparative analyses show that the proposed CNN-LSTM model outperforms conventional CNN and Deep CNN models in angle prediction. Additionally, the integration of barometric pressure data enables precise floor level detection, enhancing the system's robustness in multi-story environments. Proposed comprehensive approach significantly improves the accuracy and reliability of indoor localization, making it viable for real-world applications.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.5
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• pp.319-333
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• 2018

As human beings spend more time indoors, and with the growing complexity of indoor spaces, more focus is given to indoor spatial applications and services. 3D topological networks are used for various spatial applications that involve navigation indoors such as emergency evacuation, indoor positioning, and visualization. Manually generating indoor network data is impractical and prone to errors, yet current methods in automation need expensive sensors or datasets that are difficult and expensive to obtain and process. In this research, a methodology for semi-automatically generating a 3D indoor topological model based on IndoorGML (Indoor Geographic Markup Language) is proposed. The concept of Shooting Point is defined to accommodate the usage of omnidirectional images in generating IndoorGML data. Omnidirectional images were captured at selected Shooting Points in the building using a fisheye camera lens and rotator and indoor spaces are then identified using image processing implemented in Python. Relative positions of spaces obtained from CAD (Computer-Assisted Drawing) were used to generate 3D node-relation graphs representing adjacency, connectivity, and accessibility in the study area. Subspacing is performed to more accurately depict large indoor spaces and actual pedestrian movement. Since the images provide very realistic visualization, the topological relationships were used to link them to produce an indoor virtual tour.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.8
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• pp.5219-5226
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• 2014

This paper proposes an improved scheme of pedestrian position information system using neural network theory in a GPS-disabled area. Through a learning/obtaining gait pattern and step distance about walk, run, duck walk, crab walk and crawl, the position estimation error could be minimized by rejecting the inertial navigation drift. A portable hardware module was implemented to evaluate the performance of the proposed system. The performance and effectiveness of the suggested algorithm was verified by experiments indoors.

• Journal of Korean Society of Transportation
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• v.26 no.3
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• pp.129-141
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• 2008

Portable navigation markets grew up to 95% last year. This study aims to develop a business model for pedestrian-oriented portable navigator for street shoppers. Related previous studies developed successfully the alogrithm for a portable navigator, and the current study seeks for diverse ways to turn this system into a new type of on & off line-based LBS business. The business model proposed in this study adopted two different approaches of benefit yields based on the structural analysis of so-called Wanavi navigation system. One is segmented approach seeking for benefits from each individual system component such as communication network, contents, and platform. The other approach is to run the business by integrating relevant system componts in every possible way to attract customers to this portable navigator. The purpose of this proposal is not limited to activate emerging walking navigator markets, but to aim at creating idealistic free market system where all the shoppers are furnished complete market information to the fullest extent when they go on shopping.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.156-170
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• 2019

Rapid increasing of smartphones has changed people's lifestyles, and location-based services are providing a platform to provide various conveniences in accordance with these changes. In particular, it may provide convenience to many users if location-based services are provided in an indoor area such as subway station. However, it is still a difficult task to ensure accurate positioning result for guiding routes in subway stations. This study proposes a KAI-R system that allows all processes to be performed in one system for indoor navigation in subway stations. The proposed system includes a new pedestrian step detection method for continuous positioning along with an improved fusion positioning algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.6
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• pp.137-143
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• 2017

Facilities for pedestrian safety of the visually impaired are scarce on the walkway and crosswalk. Braille blocks are installed on the walkways and RF controlled signal lights and sound generators are installed on the crosswalk at the main intersection for the visually impaired. An RF remote control system using one frequency has a problem of simultaneously operating nearby signal lamps and sound generators at an intersection. In this paper, we develop the smart cane that uses a beacon to identify the signal lamp and sound generators installed on the crosswalk at intersections and to operate the signal lamps and sound generators in the direction of the walk by IR communication. The developed smart cane is able to provide the pedestrian navigation for the blind by interworking with mobile apps through Bluetooth communication.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1D
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• pp.21-27
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• 2008

To respond to years-long economic decline in the downtown Seoul, it is necessary to lay out a scheme revitalizing downtown economy. It is called Wanavi and Shonavi system. When customers do shopping, it is the "Shonavi" that provides shopping information such as popular shop name, location, and commodity price to walkers in a real-time base. In addition, the walking navigation system called "Wanavi" is designed to provide access information for walking shoppers, the function of which is similar to car navigation system. The "Wanavi" can help walkers find the fastest, comfortable and attractive pedestrian route and public transportation information reaching to the destination in the downtown. In this study, we propose GPA & RFID mixed system as a communication technique to activate "Shonavi" and "Wanavi" system. The "Shonavi" and "Wanavi" system will be one of the ways to revitalize depressed downtown economy in Seoul.

• Journal of Korean Port Research
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• v.13 no.1
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• pp.101-112
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• 1999

Traffic accidents increase with the increase of the vehicles in operation on the street. Especially big traffic accidents composed of over 3 killed or 20 injured accidents with the property damage become one of the serious problems to be solved in most of the cities. The purpose of this study is to build the discrimination model on big traffic accidents using the Quantification II theory for establishing the countermeasures to reduce the big traffic accidents. The results are summarized as follows. 1)The existing traffic accident related model could not explain the phenomena of the current traffic accident appropriately. 2) Based on the big traffic accident types vehicle-vehicle, vehicle-alone, vehicle-pedestrian and vehicle-train accident rates 73%, 20.5% 5.6% and two cases respectively. Based on the law violation types safety driving non-fulfillment center line invasion excess speed and signal disobedience were 48.8%, 38.1% 2.8% and 2.8% respectively. 3) Based on the law violation types major factors in big traffic accidents were road and environment, human, and vehicle in order. Those factors were vehicle, road and environment, and human in order based on types of injured driver’s death. 4) Based on the law violation types total hitting and correlation rates of the model were 53.57% and 0.97853. Based on the types of injured driver’s death total hitting and correlation rates of the model were also 71.4% and 0.59583.