• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1403-1406
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• 1997

Generally, G.P.S(Global Positioning System) is using for the car navigation system but it has some restrictions such as the discontinuity of earth satellites and SA (Selective Availability). Recently, the hybrid navigation system combining with G.P.S and Dead-reckoning are much attractuve for improving the accuracy of a vehicle positioning. G.P.S called satellite navigation system, can measure its position by using satellites. Dead-Reckoning is the self-contained navigatioin system using a wheel sensor for the vehicle velocity and a gyro sensor for the vehicle angular velocity. Some algorithm could be generated for finding the vehicle position and orientation. In this paper, we developed a hybrid algotithm wiht G.P.S DR and Map-Matching.

• Transactions of the Korean Society of Automotive Engineers
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• v.25 no.3
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• pp.367-373
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• 2017

In this paper, a new shift map was proposed to improve the fuel economy of a transmission mounted electric device(TMED) type dual clutch transmission(DCT) plug-in hybrid electric vehicle(PHEV) by considering transmission and motor losses. To construct the shift map, powertrain efficiencies of the engine-DCT-motor were obtained at each gear step. A shift map that provides the highest powertrain efficiency was constructed for the given wheel torque and vehicle speed. Simulation results showed that the fuel economy of the target PHEV can be improved by the new shift map compared with the existing engine optimal operating line(OOL) shift control.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.5
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• pp.967-974
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• 2017

In this paper, we propose an inter-symbol interference (ISI) estimation scheme based on the maximum a posteriori (MAP) algorithm for faster-than-Nyquist (FTN) systems. Unfortunately, the ISI estimator based on the MAP algorithm requires relatively high computational complexity. To reduce the complexity of the MAP based ISI estimator, we propose a hybrid ISI estimation scheme based on the MAP and successive interference cancellation (SIC) algorithms. The proposed scheme not only offers good ISI estimation performances but also requires reasonably low complexity.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.249-254
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• 2017

Digital vector maps must be compressed effectively for transmission or storage in Web GIS (geographic information system) and mobile GIS applications. This paper presents a polyline compression method that consists of polyline feature-based hybrid simplification and second derivative-based data compression. Experimental results verify that our method has higher simplification and compression efficiency than conventional methods and produces good quality compressed maps.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.3
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• pp.313-318
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• 2013

Magnetic abrasive polishing is an advanced deburring process for nonmagnetic materials and micropattern products that have non-machinability characteristics. Despite these advantages, there are some problems with using MAP for deburring. MAP has introduced geometric errors into microgrooves because of an over-cutting force caused by uncontrolled magnetic abrasives in the MAP tool. Thus, in this study, to solve this problem, an EP (electrolyte polishing)-MAP hybrid polishing process was developed for deburring microgrooves in an STS316 material. In addition, an evaluation of EP-MAP for the deburring of microgrooves was carried out by profiling the burrs. The results of the experiment showed geometric errors after the deburring process using MAP. However, in the case of EP-MAP, no geometric error was observed after the process because of the lower material removal rate in EP-MAP.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.3955-3971
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• 2020

Due to the significant increase in the use of autonomous car technology, it is essential to integrate this technology with high-precision digital map data containing more precise and accurate roadway information, as compared to existing conventional map resources, to ensure the safety of self-driving operations. While existing map technologies may assist vehicles in identifying their locations via Global Positioning System, it is however difficult to update the environmental changes of roadways in these maps. Roadway vision algorithms can be useful for building autonomous vehicles that can avoid accidents and detect real-time location changes. We incorporate a hybrid architectural design that combines unsupervised classification of vision data with supervised joint fusion classification to achieve a better noise-resistant algorithm. We identify, via a deep learning approach, an intelligent hybrid fusion algorithm for fusing multimodal vision feature data for roadway classifications and characterize its improvement in accuracy over unsupervised identifications using image processing and supervised vision classifiers. We analyzed over 93,000 vision frame data collected from a test vehicle in real roadways. The performance indicators of the proposed hybrid fusion algorithm are successfully evaluated for the generation of roadway digital maps for autonomous vehicles, with a recall of 0.94, precision of 0.96, and accuracy of 0.92.

• ETRI Journal
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• v.36 no.5
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• pp.752-760
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• 2014

This paper proposes a conditional replenishment algorithm (CRA) to improve the visual quality (where spatial resolutions of the left and right views are mismatched) of a hybrid stereoscopic 3DTV that is based on the ATSC-M/H standard. So as to generate an enhanced view, the CRA is to choose the better substitute among a disparity-compensated view with high quality and a simply interpolated view. The CRA generates a disparity map that includes modes and disparity vectors as additional information. It also employs a quad-tree structure with variable block size by considering the spatial correlation of disparity vectors. In addition, it takes advantage of the disparity map used in a previous frame to keep the amount of additional information as small as possible. The simulation results show that the proposed CRA can successfully improve the peak signal-to-noise ratio of a poor-quality view and consequently have a positive effect on the subjective quality of the resulting 3D view.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.5
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• pp.576-582
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• 2015

Generally, the fuel economy of hybrid electric vehicle (HEV) is effected by the size of each component. In this study the fuel economy for HEV of our own making is evaluated using backward simulator, where dynamic programming is applied. In a competition, the vehicle is running through the road course that includes many speed bumps and steep grade. Therefore, the new driving cycle including road grade is developed for the simulation. The backward simulator is also developed through modeling each component. A performance map of engine and motor for component sizing is made from the existing engine map and motor map adapted to the HEV of our own making. For optimal component sizing, the feasible region is defined by restricting the power range of power sources. Optimal component size for best fuel economy is obtained within the feasible region through the backward simulation.

• Journal of Drive and Control
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• v.17 no.4
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• pp.80-86
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• 2020

Autonomous driving is one of the most important new technologies of our time; it has benefits in terms of safety, the environment, and economic issues. Path following algorithms, such as automated lane keeping systems (ALKSs), are key level 3 or higher functions of autonomous driving. Pure-Pursuit and Stanley controllers are widely used because of their good path tracking performance and simplicity. However, with the Pure-Pursuit controller, corner cutting behavior occurs on curved roads, and the Stanley controller has a risk of divergence depending on the response of the steering system. In this study, we use the advantages of each controller to propose a hybrid control strategy that can be stably applied to complex driving environments. The weight of each controller is determined from the global and local curvature indexes calculated from HD map information and the current driving speed. Our experimental results demonstrate the ability of the hybrid controller, which had a cross-track error of under 0.1 m in a virtual environment that simulates K-City, with complex driving environments such as urban areas, community roads, and high-speed driving roads.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.07a
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• pp.250-251
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• 2015

본 논문에서는 자기 조직화 기능을 갖는 Kohonen의 SOM(Self organization map) 신경회로망과 주어지는 데이터에 따라 초기의 클러스터 개수를 설정하여 처리하는 수정된 K-Means 알고리즘을 결합한 Hybrid Kohonen Network 를 제안한다. 또한, 실제의 항공영상에 적용하여 고전적인 K-Means 알고리즘 및 고전적인 SOM 알고리즘보다 우수함을 보인다.