• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.5
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• pp.281-286
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• 2011

FMCW Radar sensor is commonly used for an automobile collision avoidance system for rider's safe. Systems using FMCW radar, however, would be one of expensive solutions for just simple rear obstacle detection purpose due to its high cost. In this letter, a short range rear obstacle detector using novel 24GHz AM radar sensor is presented. It can be implemented at significantly lower cost than FMCW radar for practical commercialization. The proposed AM radar sensor module is fabricated in a single aluminum housing to reduce the overall size while using single power supply voltage of 12V with 1200mA current for automotive applications. The measured detection range is up to 210cm with 10cm of distance resolution, which is suitable for a parking assistance system for automobiles.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.165-171
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• 2021

Vehicular Ad hoc Network (VANET) is an extension paradigm of moving vehicles to communicate with wireless transmission devices within a certain geographical limit without any fixed infrastructure. The vehicles have most important participation in this model is usually positioned quite dimly within the certain radio range. Fuzzy based multi-hop broadcast protocol is better than conventional message dissemination techniques in high-mobility VANETs, is proposed in this research work. Generally, in a transmission range the existing number of nodes is obstacle for rebroadcasting that can be improved by reducing number of intermediate forwarding points. The proposed protocol stresses on transmission of emergency message projection by utilization subset of surrounding nodes with consideration of three metrics: inter-vehicle distance, node density and signal strength. The proposed protocol is fuzzy MHB. The method assessment is accomplished in OMNeT++, SUMO and MATLAB environment to prove the efficiency of it.

• Journal of Drive and Control
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• v.17 no.4
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• pp.8-14
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• 2020

Intelligent devices using ICT technology have been introduced in the field of construction machinery to improve productivity and stability. Among the intelligent devices, Machine Guidance is a device that provides real-time posture, location, and work range to drivers by installing various sensors, controllers, and satellite navigation systems on construction machines. Conversely, the efficiency of equipment that requires location information, such as machine guidance, will be greatly reduced in buildings, and tunnels in the GPS blind spots. Thus, the other high-precision positioning technologies are required in the GPS blind spot zone. In this study, we will develop a relative position measurement system that provides precise location information such as construction machinery and robots in a local area where the GPS reception is difficult. A relative position measurement system tracks a marker in the form of a sphere installed on a vehicle by using the image base tracking technology, and measures the distance and direction information to the marker to calculate a position.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.665-676
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• 2022

In modern logistics, the effective use of the vehicle volume and loading capacity will reduce the logistic cost. Many heuristic algorithms can solve this knapsack problem, but lots of these algorithms have a drawback, that is, they often fall into locally optimal solutions. A fusion optimization method based on simulated annealing algorithm (SA) and binary particle swarm optimization algorithm (BPSO) is proposed in the paper. We establish a logistics knapsack model of the fusion optimization algorithm. Then, a new model of express logistics simulation system is used for comparing three algorithms. The experiment verifies the effectiveness of the algorithm proposed in this paper. The experimental results show that the use of BPSO-SA algorithm can improve the utilization rate and the load rate of logistics distribution vehicles. So, the number of vehicles used for distribution and the average driving distance will be reduced. The purposes of the logistics knapsack problem optimization are achieved.

• Journal of the Korean Society of Safety
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• v.38 no.4
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• pp.1-7
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• 2023

The steel wheels of urban railway vehicles gather a lot of data through regular measurements during maintenance. However, limited research has been carried out utilizing this data, resulting in difficulties predicting the maintenance period. This paper studied a machine learning model suitable for mileage prediction by studying the characteristics of mileage change according to diameter and flange thickness changes. The results of this study indicate that the larger the diameter, the longer the travel distance, and the longest flange thickness is at 30 mm, which gradually shortened at other times. As a result of research on the machine learning prediction model, it was confirmed that the random forest model is the optimal model with a high coefficient of determination and a low root mean square error.

• Smart Structures and Systems
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• v.32 no.2
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• pp.111-121
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• 2023

This study proposes an automated assembly performance evaluation method for prefabricated steel structures (PSSs) using machine learning methods. Assembly component images were segmented using a modified version of the receptive field pyramid. By factorizing channel modulation and the receptive field exploration layers of the convolution pyramid, highly accurate segmentation results were obtained. After completing segmentation, the positions of the bolt holes were calculated using various image processing techniques, such as fuzzy-based edge detection, Hough's line detection, and image perspective transformation. By calculating the distance ratio between bolt holes, the assembly performance of the PSS was estimated using the k-nearest neighbors (kNN) algorithm. The effectiveness of the proposed framework was validated using a 3D PSS printing model and a field test. The results indicated that this approach could recognize assembly components with an intersection over union (IoU) of 95% and evaluate assembly performance with an error of less than 5%.

• Annual Conference of KIPS
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• 2022.05a
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• pp.110-113
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• 2022

인터넷을 통해 주위 사물과 연결된 차량은 사용자에게 편리성을 제공하기 위해 다양한 콘텐츠를 요구하는데 클라우드로부터 가져오는 시간이 비교적 오래 걸리기 때문에 차량과 물리적으로 가까운 위치에 캐싱하는 기법들이 등장하고 있다. 본 논문에서는 기반 시설이 밀집하게 설치된 도시 환경에서 maximum distance separable(MDS) 코딩을 사용해 road side unit(RSU)에 캐싱하는 방법에 대해 연구하였다. RSU의 중복된 서비스 커버리지 지역을 고려하여 차량의 콘텐츠 요구에 대한 RSU hit ratio를 높이기 위해 deep Q-learning(DQN)를 사용하였다. 실험 결과 비교 알고리즘보다 hit raito 측면에서 더 높은 성능을 보이는 것을 증명하였다.

• Annual Conference of KIPS
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• 2011.11a
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• pp.318-321
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• 2011

도로 주행 시 운전을 보조하고 안전 운전을 지원하기 위한 기술인 도로상황인지 시스템에 있어 효율적인 차선 차간 검출 기법은 위의 핵심적인 기술이다. 실시간으로 수집되는 도로 상황 영상 데이터 분석에 대한 처리 시간을 단축하기 위하여 각각의 영상 프레임에 대해 관심 영역을 설정한 후 허프 변환을 적용하였다. 본 논문은 카메라로 수집되는 도로 상황 영상에 관심 영역 설정을 통한 실시간 차선 차간 인식에 관한 연구로서, 차선과 차간 인식을 위한 효율적인 알고리즘을 제안한다.

• Journal of KIBIM
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• v.13 no.4
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• pp.64-73
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• 2023

This study introduces a method of estimating the 3D coordinates of structural damage from the detection results of visual inspection provided in 2D image coordinates using sensing data of UAV and 3D shape information of BIM. This estimation process takes place in a virtual space and utilizes the BIM model, so it is possible to immediately identify which member of the structure the estimated location corresponds to. Difference from conventional structural damage localization methods that require 3D scanning or additional sensor attachment, it is a method that can be applied locally and rapidly. Measurement accuracy was calculated through the distance difference between the measured position measured by TLS (Terrestrial Laser Scanner) and the estimated position calculated by the method proposed in this study, which can determine the applicability of this study and the direction of future research.

• Journal of Korean Society of Transportation
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• v.35 no.2
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• pp.116-128
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• 2017

Traffic accidents can be defined as a physical collision event of vehicles occurred instantaneously when drivers do not perceive the surrounding vehicles and roadway environments properly. Therefore, detecting the high potential events that cause traffic accidents with monitoring the interactions among the surroundings continuously by driver is the prerequisite for prevention the traffic accidents. For the analysis, basic data were collected to analyze interactions using a test vehicle which is equipped the GPS(Global Positioning System)-IMU(Inertial Measurement Unit), camera, radar and RiDAR. From the collected data, highway geometric information and the surrounding traffic situation were analyzed and then safety evaluation algorithm for driving vehicle was developed. In order to detect a dangerous event of interaction with surrounding vehicles, locations and speed data of surrounding vehicles acquired from the radar sensor were used. Using the collected data, the tangent and curve section were divided and the driving safety evaluation algorithm which is considered the highway geometric characteristic were developed. This study also proposed an algorithm that can assess the possibility of collision against surrounding vehicles considering the characteristics of geometric road structure. The methodology proposed in this study is expected to be utilized in the fields of autonomous vehicles in the future since this methodology can assess the driving safety using collectible data from vehicle's sensors.