• Spatial Information Research
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• v.19 no.1
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• pp.83-91
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• 2011

The route finding analysis is an essential geo-related decision support tool in a LBS(Location based Services) and previous researches related to route guidance have been mainly focused on route guidances for vehicles. However, due to the recent spread of personal computing devices such as PDA, PMP and smart phone, route guidance for pedestrians have been increasingly in demand. The pedestrian route guidance is different from vehicle route guidance because pedestrians are affected more surrounding environment than vehicles. Therefore, pedestrian path finding needs considerations of factors affecting walking. This paper aimed to extract factors affecting walking and charting the factors for application factors affecting walking to pedestrian path finding. In this paper, we found various factors about environment of road for pedestrian and extract the factors affecting walking. Factors affecting walking consist of 4 categories traffic, sidewalk, network, safety facility. We calculated weights about each factor using analytic hierarchy process (AHP). Based on weights we calculated scores about each factor's attribute. The weight is maximum score of factor. These scores of factor are used to optimal pedestrian path finding as path finding cost with distance, accessibility.

• Smart Structures and Systems
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• v.24 no.6
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• pp.745-757
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• 2019

Road surface deteriorations such as potholes have caused motorists heavy monetary damages every year. However, effective road condition monitoring has been a continuing challenge to road owners. Depth cameras have a small field of view and can be easily affected by vehicle bouncing. Traditional image processing methods based on algorithms such as segmentation cannot adapt to varying environmental and camera scenarios. In recent years, novel object detection methods based on deep learning algorithms have produced good results in detecting typical objects, such as faces, vehicles, structures and more, even in scenarios with changing object distances, camera angles, lighting conditions, etc. Therefore, in this study, a Deep Learning Pothole Detector (DLPD) based on the deep region-based convolutional neural network is proposed for autonomous detection of potholes from images. About 900 images with potholes and road surface conditions are collected and divided into training and testing data. Parameters of the network in the DLPD are calibrated based on sensitivity tests. Then, the calibrated DLPD is trained by the training data and applied to the 215 testing images to evaluate its performance. It is demonstrated that potholes can be automatically detected with high average precision over 93%. Potholes can be differentiated from manholes by training and applying a manhole-pothole classifier which is constructed using the convolutional neural network layers in DLPD. Repeated detection of the same potholes can be prevented through feature matching of the newly detected pothole with previously detected potholes within a small region.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.1
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• pp.54-62
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• 2018

According to evolution of technologies, many devices related to various applications were researched. The advanced driver assistance system is a famous technique effected from the evolution. The technique of driver assistance uses image recognition methods to collect exactly information around the vehicle. The computing power of driver assistance device has become more improved than in the past. However, it's difficult that processed various recognition methods at real-time. We propose new frame skip method to process various recognition methods at real-time in the limited hardware. In the previous researches, frame skip rate was set up static values, thus the number of processed frames through recognition methods was smaller. We set up the frame skip rate dynamically using a driving condition of vehicle through speed and acceleration value, in addition, the number of processed frames was maximized. The performance is improved more 32.5% than static frame skip method.

• Journal of Internet of Things and Convergence
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• v.8 no.2
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• pp.1-6
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• 2022

This paper presents a regionalized time slotted channel hopping (TSCH) slotframe-based aerial data collection using wake-up radio. The proposed scheme aims to minimize the delay and energy consumption when an unmanned aerial vehicle (UAV) collects data from sensor devices in the large-scale service area. To this end, the proposed scheme divides the service area into multiple regions, and determines the TSCH slotframe length for each region according to the number of cells required by sensor devices in each region. Then, it allocates the cells dedicated for data transmission to the TSCH slotframe using the ID of each sensor device. For energy-efficient data collection, the sensor devices use a wake-up radio. Specifically, the sensor devices use a wake-up radio to activate a network interface only in the cells allocated for beacon reception and data transmission. The simulation results showed that the proposed scheme exhibited better performance in terms of delay and energy consumption compared to the existing scheme.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.9
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• pp.259-269
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• 2017

As emerging the '4th Industrial Revolution' by increasing the necessity of the intelligent system recently, 'Autonomic Control System' also has been the important issue. It is necessary to develop the system collecting data of machines and sensors for the autonomic control system to monitor the target system. But it is difficult to collect data because data formats of machines and sensors of the existing factories differ between each manufacturer. Therefore, this paper presents and implements data collecting and processing system that comprise 3 steps including 'ParseBuffer', 'ProcessData' and 'AddToBuffer' by using 'MTConnect' that is standard manufacturing facility data collecting middleware. Through the suggested system, we can get data in a common format usable in an autonomous control system. As a case study, we experimented with the generation and collection of AGV (Automated Guided Vehicle) data, which is an unattended transportation system in the factory. To accomplish this, we defined the data type in accordance with the MTConnect standard and confirmed the data collected through the proposed system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.61-67
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• 2023

The rise of 5G and the proliferation of smart devices have underscored the significance of multi-access edge computing (MEC). Amidst this trend, interest in effectively processing computation-intensive and latency-sensitive applications has increased. This study investigated a novel task offloading strategy considering the probabilistic MEC environment to address these challenges. Initially, we considered the frequency of dynamic task requests and the unstable conditions of wireless channels to propose a method for minimizing vehicle power consumption and latency. Subsequently, our research delved into a deep reinforcement learning (DRL) based offloading technique, offering a way to achieve equilibrium between local computation and offloading transmission power. We analyzed the power consumption and queuing latency of vehicles using the deep deterministic policy gradient (DDPG) and deep Q-network (DQN) techniques. Finally, we derived and validated the optimal performance enhancement strategy in a vehicle based MEC environment.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.3
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• pp.193-198
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• 2016

Lane change is an important issue in microscopic traffic flow simulations and active safety. Overtaking and changing lanes are dangerous driving maneuvers. This approach presents a lane-changing system based on speed and a minimum gap between vehicles in a vehicular ad hoc network (VANET). This paper proposes a solution to ensure the safety of drivers while changing lanes on highways. Efficient routing protocols could play a crucial role in VANET applications, safeguarding both drivers and passengers, and thus, maintaining a safe on-road environment. This paper focuses on the development of an intelligent transportation system that provides timely, reliable information to drivers and the concerned authorities. A test bed is created for the techniques used in the proposed system, where analysis takes place in an on-board embedded system designed for vehicle navigation. The designed system was tested on a four-lane road in Neemrana, India. Successful simulations were conducted with real-time network parameters to maximize quality of service and performance using Simulation of Urban Mobility and Network Simulator 2 (NS-2). The system implementation, together with the findings, is presented in this paper. Illustrating the approach are results from simulation using NS-2.

• The Journal of the Convergence on Culture Technology
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• v.4 no.1
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• pp.337-351
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• 2018

This paper discusses 5G IOT, Augmented Reality, Cloud Computing, Big Data, Future Autonomous Driving Vehicle technology, and presents 5G utilization of Pyeongchang Winter Olympic Games and Jeju Smart City model. The reason is that 5G is the main artery of the 4th industry.5G is the fourth industrial aorta because 5G is the core infrastructure of the fourth industrial revolution. In order for the AI, autonomous vehicle, VR / AR, and Internet (IoT) era to take off, data must be transmitted several times faster and more securely than before. For example, if you send a stop signal to LTE, which is a communication technology, to a remote autonomous vehicle, it takes a hundredth of a second. It seems to be fairly fast, but if you run at 100km / h, you can not guarantee safety because the car moves 30cm until it stops. 5G is more than 20 gigabits per second (Gbps), about 40 times faster than current LTE. Theoretically, the vehicle can be set up within 1 cm. 5G not only connects 1 million Internet (IoT) devices within a radius of 1 kilometer, but also has a speed delay of less than 0.001 sec. Steve Mollenkov, chief executive officer of Qualcomm, the world's largest maker of smartphones, said, "5G is a key element and innovative technology that will connect the future." With 5G commercialization, there will be an economic effect of 12 trillion dollars in 2035 and 22 million new jobs We can expect to see the effect of creation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.286-289
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• 2016

In this paper, we propose an architecture that affords mobile app based on nomadic smartphone using not only mobile cloud computing- architecture but also a dedicated web platform called Node.js built-in with the asynchronous, Nonblocking, Event-Driven programming paradigm. Furthermore, the design of the proposed architecture takes document oriented database known as MongoDB to deal with the large amount of data transmit by users of mobile web access application. The Node.js aims to give the programmers the tools needed to solves the large number of concurrent connections problem. We demonstrate the effectiveness of the proposed architecture by implementing an android application responsible of real time analysis by using a vehicle to applications smart phones interface approach that considers the smartphones to acts as a remote users which passes driver inputs and delivers output from external applications.

• Smart Structures and Systems
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• v.31 no.4
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• pp.311-323
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• 2023

Ever since ancient times, earthquakes have been a major threat to the civil infrastructures and the safety of human beings. The majority of casualties in earthquake disasters are caused by the damaged civil infrastructures but not by the earthquake itself. Therefore, the efficient and accurate post-earthquake assessment of the conditions of structural damage has been an urgent need for human society. Traditional ways for post-earthquake structural assessment rely heavily on field investigation by experienced experts, yet, it is inevitably subjective and inefficient. Structural response data are also applied to assess the damage; however, it requires mounted sensor networks in advance and it is not intuitional. As many types of damaged states of structures are visible, computer vision-based post-earthquake structural assessment has attracted great attention among the engineers and scholars. With the development of image acquisition sensors, computing resources and deep learning algorithms, deep learning-based post-earthquake structural assessment has gradually shown potential in dealing with image acquisition and processing tasks. This paper comprehensively reviews the state-of-the-art studies of deep learning-based post-earthquake structural assessment in recent years. The conventional way of image processing and machine learning-based structural assessment are presented briefly. The workflow of the methodology for computer vision and deep learning-based post-earthquake structural assessment was introduced. Then, applications of assessment for multiple civil infrastructures are presented in detail. Finally, the challenges of current studies are summarized for reference in future works to improve the efficiency, robustness and accuracy in this field.