• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.169-171
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• 2012

일반적인 경로 생성 기법에서는 거리가 가장 짧은 최단경로 및 시간이 적게 소요되는 빠른 경로와 같이 사용자의 이동 성향을 고려하지 않고 경로를 생성하였다. 하지만 사용자들은 시간, 거리, 도로 혼잡도와 같은 속성들에 의해 선호하는 경로가 있기 때문에 사용자의 이동 성향에 맞는 경로를 생성하는 기법들이 필요하다. 기존의 기법들은 이동 성향을 고려하여 경로를 생성하기 위해서는 사용자의 이동 성향 정보를 추가적으로 입력하여야 한다. 하지만 네비게이션 및 모바일 장치의 불편한 인터페이스 특성상 이러한 정보 입력은 거의 하지 않고 출발지와 목적지만을 입력하여 경로를 추천 받는 경향이 있다. 따라서 본 논문에서는 추가적인 이동 성향에 대한 정보 입력 없이 과거 궤적 데이터를 통해 사용자의 이동 성향을 암시적으로 추출하고 이동 성향에 맞는 경로를 생성하는 기법을 제안한다. 성능평가를 통해 최소 시간 경로나 최단 거리 경로와 비교하여 제안하는 기법이 사용자의 이동 성향을 고려한 경로가 생성됨을 입증한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.768-773
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a drowsiness detection system using Linear Predictive Coding (LPC) coefficients and Support Vector Machine (SVM). Samples of EEG data from each predefined state were used to train the SVM program by using the proposed feature extraction algorithms. The trained SVM program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.4
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• pp.158-168
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• 2009

As nation and society progresses, urban ground facilities and their management system get more complicated and the cost and effort to control the system efficiently grows exponentially. This study suggests to the deployment method of a sensor node by Wireless Sensor Network for controling the Urban Ground Facilities of National Facilities. First, we achieve the management facilities and method using the first analysis and then make the coverage of sensing and then set up the Sensor Node in Urban Ground Facilities. Second, we propose the solution way of repetition by the second analysis. And, we embody the GIS program by Digital Map and this method, we improve the reality by overlapping an aerial photo. Also we make an experience on the sensor node allocation using making program. we can remove the repetition sensor node about 50%, and we can confirm that the sensor nodes are evenly distributed on the road.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.4
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• pp.57-62
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• 2014

Recently, research activities to develop services for providing safety information to the drivers in fast moving vehicles based on various wireless network technologies such as DSRC (Dedicated Short Range Communication), IEEE 802.11p WAVE (Wireless Access for Vehicular Environment) are widely being carried out. This paper presents a proof-of-concept model based on a robot-car for Dilemma Zone Decision Assistant Service using the wireless LAN technology. The proposed model system consists of a robot-car based on an embedded Linux OS equipped with a WiFi interface and an on-board unit emulator, an Android-based remote controller to model a human driver interface, a laptop computer to run a model traffic signal controller and signal lights, and a WiFi access point to model a road-side unit.

• Journal of the Economic Geographical Society of Korea
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• v.22 no.3
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• pp.321-336
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• 2019

The ICT industry has developed into an industry that generates national competitiveness. The policy to support the ICT industry, initiated by the government, has been activated mainly on the Teheran Road of Gangnam-gu in Seoul. In this regard, this study examined the emergence background and formation factors of the new innovation district in the urban area of the Teheran Valley. In addition, this study examined the characteristics of the innovation district in the integrated urban and industrial parks with various facilities and institutions supporting the startup. This study examined the status of the Teheran Valley as an urban innovation district by dividing the indicators of the urban innovation district in the Teheran Valley into economic assets, spatial and physical assets, network assets, and human capital. It also examined the ICT technology leading the innovation and analyzed the implications for regional economic development.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.245-248
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• 2009

Recently, Many studies have been progressing to path-finding with dynamic information on the Terminal Based Navigation System(TBNS). However, the most of existing algorithms are based on $A{\ast}$ algorithm. Path-finding algorithms which use heuristic function may occur a problem of the increase of exploring cost in case of that there is no way determined by heuristic function or there are 2 way more which have almost same cost. In this paper, two abstract graph(AG) that are different method of construction, Homogeneous Node merging($AG^H$) and Connected Node Merging($AG^C$), are implemented. The abstract graph is a simple graph of real road network. The method of using the abstract graph is proposed for reducing dependency of heuristic and exploring cost. In result of evaluation of performance, $AG^C$ has better performance than $AG^H$ at construction cost but $AG^C$ has worse performance than $AG^H$ exploring cost.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.4
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• pp.445-450
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• 2018

AIS is the most important navigation equipment for the identification of other ships, etc. However, the AIS overload problem has been raised recently due to an increase in AIS equipped vessels. The government is planning to introduce the wireless LTE network at 100 km offshore as part of the SMART-Navigation project. Continuous development and dissemination of the services available through such platforms will be necessary to achieve major goals such as marine accident prevention and environmental protection. In this study, we applied a WAVE communication system, which could be the basis for the development of such services. As a result, reliable data transmission was confirmed for a range of communication of approx. 5 miles, although the service was limited to 1 km in road traffic. Therefore, it is expected that WAVE communication technology will be used to prevent marine accidents through such efforts as collision avoidance and the transfer of marine safety information between ships.

• Journal of Korea Multimedia Society
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• v.22 no.1
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• pp.44-54
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• 2019

Recently, image-based object detection has made great progress with the introduction of Convolutional Neural Network (CNN). Many trials such as Region-based CNN, Fast R-CNN, and Faster R-CNN, have been proposed for achieving better performance in object detection. YOLO has showed the best performance under consideration of both accuracy and computational complexity. However, these data-driven detection methods including YOLO have the fundamental problem is that they can not guarantee the good performance without a large number of training database. In this paper, we propose a data sampling method using CycleGAN to solve this problem, which can convert styles while retaining the characteristics of a given input image. We will generate the insufficient data samples for training more robust object detection without efforts of collecting more database. We make extensive experimental results using the day-time and night-time road images and we validate the proposed method can improve the object detection accuracy of the night-time without training night-time object databases, because we converts the day-time training images into the synthesized night-time images and we train the detection model with the real day-time images and the synthesized night-time images.

• 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 Korea Convergence Society
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• v.12 no.1
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• pp.29-35
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• 2021

License plate recognition plays a key role in intelligent transportation systems. Therefore, it is a very important process to efficiently detect the number and character areas. In this paper, we propose a method to effectively detect license plate number area by applying deep learning and semantic image segmentation algorithm. The proposed method is an algorithm that detects number and text areas directly from the license plate without preprocessing such as pixel projection. The license plate image was acquired from a fixed camera installed on the road, and was used in various real situations taking into account both weather and lighting changes. The input images was normalized to reduce the color change, and the deep learning neural networks used in the experiment were Vgg16, Vgg19, ResNet18, and ResNet50. To examine the performance of the proposed method, we experimented with 500 license plate images. 300 sheets were used for learning and 200 sheets were used for testing. As a result of computer simulation, it was the best when using ResNet50, and 95.77% accuracy was obtained.