• 대한원격탐사학회지
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• 제39권1호
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• pp.77-86
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• 2023

Ship detection is widely used in areas such as maritime security, maritime traffic, fisheries management, illegal fishing, and border control, and ship detection is important for rapid response and damage minimization as ship accident rates increase due to recent increases in international maritime traffic. Currently, according to a number of global and national regulations, ships must be equipped with automatic identification system (AIS), which provide information such as the location and speed of the ship periodically at regular intervals. However, most small vessels (less than 300 tons) are not obligated to install the transponder and may not be transmitted intentionally or accidentally. There is even a case of misuse of the ship'slocation information. Therefore, in this study, ship detection was performed using high-resolution optical satellite images that can periodically remotely detect a wide range and detectsmallships. However, optical images can cause false-alarm due to noise on the surface of the sea, such as waves, or factors indicating ship-like brightness, such as clouds and wakes. So, it is important to remove these factors to improve the accuracy of ship detection. In this study, false alarm wasreduced, and the accuracy ofship detection wasimproved by removing wake.As a ship detection method, ship detection was performed using machine learning-based random forest (RF), and convolutional neural network (CNN) techniquesthat have been widely used in object detection fieldsrecently, and ship detection results by the model were compared and analyzed. In addition, in this study, the results of RF and CNN were combined to improve the phenomenon of ship disconnection and the phenomenon of small detection. The ship detection results of thisstudy are significant in that they improved the limitations of each model while maintaining accuracy. In addition, if satellite images with improved spatial resolution are utilized in the future, it is expected that ship and wake simultaneous detection with higher accuracy will be performed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.999-1021
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• 2023

Differential spatial modulation uses the antenna index to transmit information, which improves the spectral efficiency, and completely bypasses any channel side information in the recommended setting. A generalized distributed multiple turbo coded-cooperative differential spatial modulation based on distributed multiple turbo code is put forward and its performances in Rayleigh fading channels is analyzed. The generalized distributed multiple turbo coded-cooperative differential spatial modulation scheme is a coded-cooperation communication scheme, in which we proposed a new joint parallel iterative decoding method. Moreover, the code matched interleaver is considered to be the best choice for the generalized multiple turbo coded-cooperative differential spatial modulation schemes, which is the key factor of turbo code. Monte Carlo simulated results show that the proposed cooperative differential spatial modulation scheme is better than the corresponding non-cooperative scheme over Rayleigh fading channels in multiple input and output communication system under the same conditions. In addition, the simulation results show that the code matched interleaver scheme gets a better diversity gain as compared to the random interleaver.

• 한국측량학회지
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• 제39권1호
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• pp.29-40
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• 2021

범죄, 화재, 교통사고 등 국민의 안전을 위협하는 위험인자들은 지역적 맥락과 공간적 특성을 가지고 있다. 지역마다 서로 다른 위험환경을 가지고 있으므로 교통사고, 화재, 범죄, 생활안전 분야별로 위험요소의 공간적 패턴을 분석할 필요가 있다. 본 연구는 전국 기초자치단체를 대상으로 분야(교통사고, 화재, 범죄, 생활안전, 자살, 감염병)별 안전등급을 측정한 지표인 지역안전지수의 공간적 분포 패턴을 분석하는데 연구의 목적이 있다. 지역안전지수의 공간적 자기상관성 분석을 위해 전역적 공간자기상관분석(Global Moran's I)과 Local Moran's I를 활용한 LISA(Local Indicators of Spatial Association) 분석, Getis-Ord's G⁎i 분석을 실시하였다. 분석결과 교통사고, 화재, 자살의 안전지수 분포는 범죄, 생활안전, 감염병의 안전지수보다 공간적으로 집중(clustered) 경향을 보였다. 지역간 유의미한 공간적 연관성을 분석한 LISA 분석결과에 따르면, 수도권 지역이 다른 도시에 비하여 지역안전통합지수를 기준으로 비교적 안전한 지역인 것으로 나타났다. 또한 Getis-Ord's G⁎i 통계값을 활용한 핫스팟분석 결과 안전 취약지역의 군집인 3개의 핫스팟(강원도 삼척시, 경상북도 청송군, 전라북도 김제시)과 전반적인 안전 수준이 높은 군집인 15개의 콜드스팟이 도출되었다. 이러한 연구 결과는 안전 수준 취약지역의 공간적 분포와 패턴을 파악하여 안전 지수 개선을 위한 정책 수립시 기초자료로 활용될 수 있다.

• 대한교통학회지
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• 제30권3호
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• pp.31-41
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• 2012

VMS, 교통방송(라디오), SNS 등 교통정보제공을 통한 교통량 분산에 대한 관심이 높아짐에 따라 교통정보에 대한 운전자의 반응행태 및 효과에 대한 연구가 지속적으로 이루어지고 있다. 본 연구는 FTMS 및 TCS 데이터가 구축되어 있는 전국 고속도로 본선부를 분석 대상으로, FTMS 데이터와 TCS 데이터를 이용하여 경로전환 교통량과 그에 따른 경로전환율을 추정하는 방법론을 제시하고, 실제 대상구간의 경로전환율을 산출하여 시간 및 공간적 경로 전환율 변동에 대한 특성을 분석하였다. 본 연구에서는 교통정보 제공 이후, 해당 시간대의 평균 유출교통량에 비해 유출교통량이 일시적으로 증가한 경우 이 편차(도로교통상황의 동적인 특성으로 인한 편차 고려)는 교통정보 제공으로 인한 경로전환 교통량이라 정의하고, 본선 교통량과의 비율을 경로전환율로 계산하였다. 시간흐름에 따른 경로전환율 변화를 분석한 결과, 혼잡상황에 대한 교통정보를 먼저 얻게 되는 상류부 IC에서의 초기 경로전환율(유출교통량) 변화는 일시적으로 매우 큰 것으로 나타났다. 이후 공간적 시간적 흐름에 따라 상류부 IC에서의 경로전환율의 변화는 하류부 IC에서의 경로전환에 영향을 미치고, 이는 다시 상류부 IC에서의 변화를 유도하는 등의 경로전환 순환체계(feedback control loop)가 있음을 확인하였다.

• 한국정보통신학회논문지
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• 제25권12호
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• pp.1723-1728
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• 2021

C-ITS는 차량, 도로 인프라, 운전자, 보행자 등 구성요소 간 교통정보를 수집·관리·제공함으로써 교통 이용 편의, 교통안전을 제고 할 수 있는 지능형교통시스템이다. 국내에서는 C-ITS 사업을 통해 도로인프라가 국내 전역에 구축되고, 실시간 교통정보 제공, 버스운행 관리 등 다양한 서비스가 제공되고 있다. 그러나 현재 도로상황 인지·전파 등을 통한 교통안전 중심의 C-ITS를 구축하기에는 첨단도로 인프라 및 정보 연계 체계가 미흡한 실정이다. 본 논문에서는 다양한 공간적 측면에서 시간의 연속성을 고려하여, 교통 인프라 간 정보 연계를 통해 교통흐름과 돌발 사고를 인지할 수 있는 그룹형 Zigbee Mesh 네트워크 기반 교통상황인지 시스템을 제시하였다. 제안하는 시스템은 1차적으로 현장에서 사고감지 및 경보 등의 대응이 가능하며, 타 시스템과의 정보 연계를 통해 보다 다양한 교통정보 서비스로의 활용이 가능할 것으로 기대한다.

• Journal of information and communication convergence engineering
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• 제13권2호
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• pp.105-112
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• 2015

The estimation of missing sensor values is an important problem in sensor network applications, but the existing approaches have some limitations, such as the limitations of application scope and estimation accuracy. Therefore, in this paper, we propose a new estimation model based on a spatial-temporal correlation analysis (STCAM). STCAM can make full use of spatial and temporal correlations and can recognize whether the sensor parameters have a spatial correlation or a temporal correlation, and whether the missing sensor data are continuous. According to the recognition results, STCAM can choose one of the most suitable algorithms from among linear interpolation algorithm of temporal correlation analysis (TCA-LI), multiple regression algorithm of temporal correlation analysis (TCA-MR), spatial correlation analysis (SCA), spatial-temporal correlation analysis (STCA) to estimate the missing sensor data. STCAM was evaluated over Intel lab dataset and a traffic dataset, and the simulation experiment results show that STCAM has good estimation accuracy.

• Journal of Communications and Networks
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• 제16권2호
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• pp.202-208
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• 2014

Wireless network information services allow end systems to discover heterogeneous networks and spectrum available for secondary use at or near their current location, helping them to cope with increasing traffic and finite spectrum resources. We propose a unified architecture that allows end systems to find nearby base stations that are using either licensed, shared or unlicensed spectrum across multiple network operators. Our study evaluates the performance and scalability of spatial databases storing base station coverage area geometries. The measurement results indicate that the current spatial databases perform well even when the number of coverage areas is very large. A single logical spatial database would likely be able to satisfy the query load for a large national cellular network. We also observe that coarse geographic divisions can significantly improve query performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.1975-1988
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• 2019

Short-term traffic flow prediction plays an important role in intelligent transportation systems (ITS) in areas such as transportation management, traffic control and guidance. For short-term traffic flow regression predictions, the main challenge stems from the non-stationary property of traffic flow data. In this paper, we design an ensemble cascading prediction framework based on extremely randomized trees (extra-trees) using a boosting technique called EET to predict the short-term traffic flow under non-stationary environments. Extra-trees is a tree-based ensemble method. It essentially consists of strongly randomizing both the attribute and cut-point choices while splitting a tree node. This mechanism reduces the variance of the model and is, therefore, more suitable for traffic flow regression prediction in non-stationary environments. Moreover, the extra-trees algorithm uses boosting ensemble technique averaging to improve the predictive accuracy and control overfitting. To the best of our knowledge, this is the first time that extra-trees have been used as fundamental building blocks in boosting committee machines. The proposed approach involves predicting 5 min in advance using real-time traffic flow data in the context of inherently considering temporal and spatial correlations. Experiments demonstrate that the proposed method achieves higher accuracy and lower variance and computational complexity when compared to the existing methods.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.280-282
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• 2004

According as IT environment changes from client/server system to the open system based on web service technologies, the exchange of data and services between independent applications and service servers in web environment is observed Recently. In this points, it is necessary that Geographic Information Systems, Spatial Imagery Information Systems, Intelligent Transportation Systems, and Global Navigation Satellite Systems based on the location and geometry information are inter-related each other. But, the inter-relation of these systems is very difficult because of the closed system architecture, different information format, and the absence of interoperability technologies. In this Paper, we describe 4S integration system based web service technologies that accept the international standard as WFS, WCS, WRS of OGC (OpenGIS Consortium) and UDDI, SOAP, WSDL of W3C. In detail, we study the system architecture design and implementation of traffic information service, routing service, and tracing service. These services are consisting of the data schema based on XML and GML, various functions of each layered service, each server platform, and web server. Specially, we represent that GML is being applied to a wide range of location-based services, telematics, and intelligent transportation

• 대한원격탐사학회지
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• 제18권5호
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• pp.263-271
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• 2002

Recently, spatial information technologies using remotely sensed imagery and functionality of GIS (Geographic Information Systems) have been widely utilized to various types of transportation-related applications. In this study, extraction programs of some practical indices, to be effectively used in transportation reference planning problem, were designed and implemented as prototyped extensions in GIS development environment: traffic flow estimation (TFL/TFB), urban rural index (URI), and accessibility index (AI). In TFL/TFB, user can obtain quantitative results on traffic flow estimation at link/block using high-resolution satellite imagery. Whereas, URI extension provides urban-rural characteristics related to road system, being considered one of important factors in transportation planning. Lastly, AI extension helps to obtain accessibility index between nodes of road segments and surrounding district areas touched or intersected with the road network system, and it also provides useful information for transportation planning problems. This approach is regarded as one of RS-T (Remote Sensing in Transportation), and it is expected to expand as new application of remotely sensed imagery.