• Transactions of the Korean Society of Automotive Engineers
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• v.17 no.5
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• pp.84-90
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• 2009

The bi-modal tram is under development as a new public transportation. The features of the tram are an extended wheel base and its length. This features result in difficulties for drivers on maneuvering the tram. Therefore, the all wheel steering system is applied to the articulated vehicle. The AWS system enables the vehicle to steer all the rear wheels independently and improves its driving characteristics. However, the bi-modal tram has a problem to move backward in the limited place because of its geometric feature and the AWS system. Hence, the reverse parking assistant algorithm for articulated vehicle is developed to solve the problems of the reverse parking. Using the vehicle model which includes the reverse parking assistant algorithm, the dynamic analysis is performed for several parking cases. By the result of the analysis, the stability and validity of the reverse parking assistant algorithm is verified.

• Transactions of the Korean Society of Automotive Engineers
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• v.16 no.1
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• pp.144-151
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• 2008

The bi-modal vehicle is composed of two car-bodies and three axles. Each axle of the vehicle has an independent suspension and all wheels are steerable. Since the bi-modal vehicle has longer wheelbase than most urban buses, the All-Wheel-Steering(AWS) system is adapted for to ensure safe driving and proper turning radius on a curved road. This paper proposes an AWS control algorithm for stable driving of bi-modal vehicle. Steering angles and directions of each axle of bi-modal vehicle changed according to the driving environment and steering modes. In the case that front and rear axles should be steered in opposite directions is a negative mode, and the other case that the axles should be steered in the same direction is a positive mode. For example, in the positive mode, front and real axles are steered in the same direction, while in the negative mode, they are steered in the opposite direction. A multibody model of the vehicle is used to verify the performance of the steering algorithm and simulation results of 2WS are compared with those of AWS under the same condition.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.283-290
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• 2007

A bi-modal tram has been developed to offer an advanced transportation service compared with existing vehicles. The All-Wheel-Steering system is applied to the bi-modal tram to satisfy the required steering performance because the bi-modal tram has extended length and articulated mechanism. An ECU for the steering system is essential to steer wheels on 2nd and 3rd axles by the specific AWS algorithm with the prescribed driving condition. The Hardware-In-the-Loop Simulation(HILS) system is planned for the purpose of evaluating the steering system of the bi-modal tram. There are kinematic links with the hydraulic actuator to steer wheels on each 2nd and 3rd axles and also same steering mechanism as the actual vehicle is in the HILS system. Controlling the movement of hydraulic actuator which reflects the lateral steering reaction force on each wheel is the key to realize the HILS system, but the reaction force is continuously changed according to various driving conditions. Therefore, the simulation through the multi-body dynamics model is used to obtain the required forces.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.987-988
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• 2008

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.06a
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• pp.829-830
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• 2007

• Journal of Navigation and Port Research
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• v.40 no.5
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• pp.265-270
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• 2016

Extreme tropospheric anomalies such as typhoons or regional torrential rain can degrade positioning accuracy of the GPS signal. It becomes one of the main error terms affecting high-precision positioning solutions in network RTK. This paper proposed a detection algorithm to be used during atmospheric anomalies in order to detect the tropospheric irregularities that can degrade the quality of correction data due to network errors caused by inhomogeneous atmospheric conditions between multi-reference stations. It uses an atmospheric grid that consists of four meteorological stations and estimates the troposphere zenith total delay difference at a low performance point in an atmospheric grid. AWS (automatic weather station) meteorological data can be applied to the proposed tropospheric anomaly detection algorithm when there are different atmospheric conditions between the stations. The concept of probability density distribution of the delta troposphere slant delay was proposed for the threshold determination.

• Journal of KIISE
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• v.41 no.9
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• pp.686-698
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• 2014

Recently, large-scale streaming sensor data have emerged due to explosive supply of smart phones, diffusion of IoT and Cloud computing technology, and generalization of IoT devices. Also, researches on combination of semantic web technology are being actively pushed forward by increasing of requirements for creating new value of data through data sharing and mash-up in large-scale environments. However, we are faced with big issues due to large-scale and streaming data in the inference field for creating a new knowledge. For this reason, we propose the RDFS rule based parallel reasoning scheme to service by processing large-scale streaming sensor data with the semantic web technology. In the proposed scheme, we run in parallel each job of Rete network algorithm, the existing rule inference algorithm and sharing data using the HBase, a hadoop database, as a public storage. To achieve this, we implement our system and evaluate performance through the AWS data of the weather center as large-scale streaming sensor data.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.101-104
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• 2008

In this study, rainfall adjust and forecasting using artificial neural network(ANN) which includes a correlation coefficient is application in Seoul region. It analyzed one-hour rainfall data which has been reported in 25 region in seoul during from 2000 to 2006 at rainfall observatory by AWS. The ANN learning algorithm apply for input data that each region using cross-correlation will use the highest correlation coefficient region. In addition, rainfall adjust analyzed the minimum error based on correlation coefficient and determination coefficient related to the input region. ANN model used back-propagation algorithm for learning algorithm. In case of the back-propagation algorithm, many attempts and efforts are required to find the optimum neural network structure as applied model. This is calculated similar to the observed rainfall that the correlation coefficient was 0.98 in missing rainfall adjust at 10 region. As a result, ANN model has been for suitable for rainfall adjust. It is considered that the result will be more accurate when it includes climate data affecting rainfall.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.63-71
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• 2020

In this paper, we propose a fire detection technology using YOLOv3 and EfficientDet, the most reliable artificial intelligence detection algorithm recently, an alert service that simultaneously transmits four kinds of notifications: text, web, app and e-mail, and an AWS system that links fire detection and notification service. There are two types of our highly accurate fire detection algorithms; the fire detection model based on YOLOv3, which operates locally, used more than 2000 fire data and learned through data augmentation, and the EfficientDet, which operates in the cloud, has conducted transfer learning on the pretrained model. Four types of notification services were established using AWS service and FCM service; in the case of the web, app, and mail, notifications were received immediately after notification transmission, and in the case of the text messaging system through the base station, the delay time was fast enough within one second. We proved the accuracy of our fire detection technology through fire detection experiments using the fire video, and we also measured the time of fire detection and notification service to check detecting time and notification time. Our AI fire detection and notification service system in this paper is expected to be more accurate and faster than past fire detection systems, which will greatly help secure golden time in the event of fire accidents.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.201-203
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• 2004

We present here, some of the studies carried for estimation of rainfall over land and oceanic regions in and around South Korea. We use active and passive microwave measurements from TRMM - TMI and Precipitation Radar (PR) respectively during a typhoon even named - RUSA that took place during 30 Aug. 2002. We have followed due approach by Yao at. all (2002) and examined the performance of their algorithm using two main predictor variable, named as Scattering Index (SI) and Polarization Corrected Brightness Temperature (PCT) while using TMI data. The rainfall rate estimated using PCT and SI shows some under-estimation as compared to the AWS rainfall products from the PR in common area of overlap. A larger database thus would be used in future. To establish a new rain rate algorithm over Korean region based on the present case study.