• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.1 no.1
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• pp.41-51
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• 2002

As Advanced Traveler Information System(ATIS) is the kernel of the Intelligent Transportation System, it is very important how to manage data from traffic information collectors on a road and have at borough grip of the travel time's change quickly and exactly for doing its part. Link travel time can be obtained by two method. One is measured by area detection systems and the other is estimated by point detection systems. Measured travel time by area detection systems has the limitation for real time information because it Is calculated by the probe which has already passed through the link. Estimated travel time by point detection systems is calculated by the data on the same time of each. section, this is, it use the characteristic of the various cars of each section to estimate travel time. For this reason, it has the difference with real travel time. In this study, Artificial Neural Networks is used for estimating link travel time concerned about the relationship with vehicle detector data and link travel time. The method of estimating link travel time are classified according to the kind of input data and the Absolute value of error between the estimated and the real are distributed within 5$\~$15minute over 90 percent with the result of testing the method using the vehicle detector data and AVI data of Namsan Tunnel $\#$1. It also reduces Time lag of the information offered time and draws late delay generation and dissolution.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.6
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• pp.1-14
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• 2019

Carbon storage is one of the regulating ecosystem services provided by urban street trees. It is important that evaluating the economic value of ecosystem services accurately. The carbon storage of street trees was calculated by measuring the morphological parameter on the field. As the method is labor-intensive and time-consuming for the macro-scale research, remote sensing has been more widely used. The airborne Light Detection And Ranging (LiDAR) is used in obtaining the point clouds data of a densely planted area and extracting individual trees for the carbon storage estimation. However, the LiDAR has limitations such as high cost and complicated operations. In addition, trees change over time they need to be frequently. Therefore, Structure from Motion (SfM) photogrammetry with unmanned Aerial Vehicle (UAV) is a more suitable method for obtaining point clouds data. In this paper, a UAV loaded with a digital camera was employed to take oblique aerial images for generating point cloud of street trees. We extracted the diameter of breast height (DBH) from generated point cloud data to calculate the carbon storage. We compared DBH calculated from UAV data and measured data from the field in the selected area. The calculated DBH was used to estimate the carbon storage of street trees in the study area using a regression model. The results demonstrate the feasibility and effectiveness of applying UAV imagery and SfM technique to the carbon storage estimation of street trees. The technique can contribute to efficiently building inventories of the carbon storage of street trees in urban areas.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.562-565
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. However, this irregular weight distribution is not possible to be recognized with the current weight measurement system for vehicles on roads. To address this limitation, we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles from the CCTV, black box, and hand-held camera point of view. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data. From the result, we believe that public big data can be utilized more efficiently and applied to the development of an object detection-based overloaded vehicle detection model.

• Korean Journal of Remote Sensing
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• v.30 no.6
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• pp.829-837
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• 2014

The KOMPSAT-3 can acquire panchromatic stereo image with 0.7 m spatial resolution, and provides Rational Polynomial Coefficient (RPC). In order to determine ground coordinate using the provides RPC, which include interior-exterior orientation errors, its adjustment is needed by using the Ground Control Point (GCP). Several thousands of national Unified Control Points (UCPs) are established and overall distributed in the country by the Korean National Geographic Information Institute (NGII). UCPs therefore can be easily searched and downloaded by the national-control-point-record-issues system. This paper introduced the point-extraction method and the distance-bearing method to detect of UCPs. As results, the distance-bearing method was better detected through the experiment. RPC adjustment using this method was compared with that by only one UCP and GCPs using GPS. The proposed method was more accurate than the other method in the horizontal. As demonstrated in this paper, the proposed UCPs detection method could be replaced GPS surveying for RPC adjustment.

• Journal of Internet Computing and Services
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• v.6 no.3
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• pp.159-170
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• 2005

OC-SVM(One Class Support Vector Machine) avoids solving a full density estimation problem, and instead focuses on a simpler task, estimating quantiles of a data distribution, i.e. its support. OC-SVM seeks to estimate regions where most of data resides and represents the regions as a function of the support vectors, Although OC-SVM is powerful method for data description, it is difficult to incorporate human subjective importance into its estimation process, In order to integrate the importance of each point into the OC-SVM process, we propose a fuzzy version of OC-SVM. In FOC-SVM (Fuzzy One-Class Support Vector Machine), we do not equally treat data points and instead weight data points according to the importance measure of the corresponding objects. That is, we scale the kernel feature vector according to the importance measure of the object so that a kernel feature vector of a less important object should contribute less to the detection process of OC-SVM. We demonstrate the performance of our algorithm on several synthesized data sets, Experimental results showed the promising results.

• Proceedings of the KIEE Conference
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• 2004.07c
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• pp.1857-1859
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• 2004

It is widely known that the ultra high frequency (UHF) method that detects the electromagnetic wave of the PD pulses in the gas insulated space is one of the most competitive methods for its high sensitivity. From the above point of view, this paper describes the noise suppression methods and the PD measurement results of the in-service substation by the developed UHF PD measurement system which consists of the external UHF coupler, the UWB LNA and the digital storage oscilloscope. As results, it was found that the effect of the noise suppression methods were verified and that the developed external UHF coupler showed a better detection sensitivity than a conventional external coupler.

• Proceedings of the KIEE Conference
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• 2004.07c
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• pp.1860-1862
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• 2004

It is widely known that the ultra high frequency (UHF) method that detects the electromagnetic wave of the PD pulses in the gas insulated space is one of the most competitive methods for its high sensitivity. From the above point of view, this paper describes the characteristics of GIS PD signals measured with ultra wide band (UWB) GIS PD detecting system in which PD signals are detected into the dual UHF band. The UWB PD detection system consists of the UWB UHF coupler, the UWB low noise amplifier (LNA) and the oscilloscope. The dual bands for PD signals are 0.5-2GHz(full band) and 1-2GHz(high band). As results, it was found that the partial discharges of each defect have their own characteristic pattern and the ratio of High band to Full band increases with gas pressure.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.1
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• pp.41-53
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• 2017

A thermal camera is used to obtain thermal information of a certain area. However, it is difficult to depict all the information of an area in an individual thermal image. To form a high-resolution panoramic thermal image, we propose an optimized FAST (feature from accelerated segment test) algorithm to combine two or more images of the same scene. The FAST is an accurate and fast algorithm that yields good positional accuracy and high point reliability; however, the major limitation of a FAST detector is that multiple features are detected adjacent to one another and the interest points cannot be obtained under no significant difference in thermal images. Our proposed algorithm not only detects the features in thermal images easily, but also takes advantage of the speed of the FAST algorithm. Quantitative evaluation shows that our proposed technique is time-efficient and accurate. Finally, we create a mosaic of the video to analyze a comprehensive view of the scene.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06c
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• pp.47-50
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• 1998

본 논문은 자동차의 편의성 및 안전성의 동시 확보를 위하여, 보조적 스위치의 조작없이 상시 음성의 입,출력이 가능하도록 하며, band pass filter를 이용하여 잡음환경에서 자동으로 정확하게 음성구간 검출(End Point Detection)을 하게 하였다. Reference Pattern은 Dynamic Multi-Section(DMS)[1] 모델을 사용하였고 차량의 속도에 따라 자동으로 잡음환경에 강인한 모델을 선택하도록 하였으며, 음성의 특징 파라미터와 인식 알고리즘은 Perceptual Linear Predictive(PLP) 13차와 One Stage Dynamic Programming(OSDP)를 사용하였다. 주행중인 자동차 환경(30~70km/h)에서 자주 사용되는 차량제어 명령 33개에 대하여 화자독립 92.98%, 화자종속 94.44% 인식율을 구하였다. 또한 주행중인 차량에서 카폰, 핸드폰 사용으로 인한 사고를 줄이기 위하여 음성으로 전화를 걸 수 있도록 하는 Voice Dialing 기능도 구현하였다.

• Annals of Clinical Neurophysiology
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• v.10 no.1
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• pp.25-28
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• 2008

Diabetic polyneuropathy (DPN) is the most frequently encountered form of neuropathy in diabetic patients, and it either relentlessly progresses or remains relatively stable for many years, not showing any trend towards improvement. From this point of view, early detection of DPN is very important to prevent the irreversible change of the peripheral nerve from diabetic insults. Although a number of clinical symptoms and/or deficit scales have been developed for clinical or research purposes, nerve conduction study (NCS) has been known one of the most objective and sensitive tools to detect peripheral nerve dysfunctions in diabetic patients. NCS, however, also have several shortcomings. The next two consecutive articles will focus on debates about diagnostic usefulness of NCS and on recent updates of other diagnostic tests including quantitative sensory testings and skin biopsy in the field of diabetic polyneuropathy.