• Journal of Drive and Control
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• v.17 no.1
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• pp.13-20
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• 2020

Adaptive neural networks based lateral controller is presented to guarantee path following performance for vehicle lane keeping in the presence of parameter time-varying characteristics of the vehicle lateral dynamics due to the road surface condition, load distribution, tire pressure and so on. The proposed adaptive controller could compensate vehicle lateral dynamics deviated from nominal dynamics resulting from parameter variations by incorporating it with neural networks that have the ability to approximate any given nonlinear function by adjusting weighting matrices. The controller is derived by using Lyapunov-based approach, which provides adaptive update rules for weighting matrices of neural networks. To show the superiority of the presented adaptive neural networks controller, the simulation results are given while comparing with backstepping controller chosen as the baseline controller. According to the simulation results, it is shown that the proposed controller can effectively keep the vehicle tracking the pre-given trajectory in high velocity and curvature with much accuracy under parameter variations.

• Transactions of the Korean Society of Automotive Engineers
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• v.6 no.5
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• pp.146-152
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• 1998

Recently, for the development of vehicle structures and components there is a tendency to increase using numerical simulation methods compared with practical tests for the estimation of the fatigue strength. In this study, an integrated powerful methodology is suggested for fatigue strength evaluation through development of the interface program to integrate dynamic analysis quasi-static stress analysis and fatigue analysis, which were so far used independently. To verify the presented evaluation method, a single and zigzag bump run test, 4-post road load simulation and driving durability test have been performed. The prediction results show a good agreement between analysis and test. This research indicates that the integrated life prediction methodology can be used as a reliable design tool in the pre-prototype and prototype development stage, to reduce the expense and time of design iteration.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.09a
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• pp.473-485
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• 2010

Lightweight materials using in-situ clay soil contain large amounts of fine grain and cement for increasing the strength, lighter weight to increase liquidity for the foam and the bulk of the material is conducted by the water. Domestic cases, Light weight soil to improve cementation and lightness using demountable mixing device is defined Smartsoil. Typical features are their self-leveling, self-compaction, folwability. By adjusting the amount of cement, the strength can be controlled artificially. And re-excavation is easy. In this paper, pre-loading method using the road due to the displacement of adjacent structures under construction as an alternative SmartSoil introduces the design and construction practices. Is to discuss and improve.

• The Journal of Korea Robotics Society
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• v.5 no.4
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• pp.339-348
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• 2010

One of the requirements for autonomous vehicles on off-road is to move stably in unstructured environments. Such capacity of autonomous vehicles is one of the most important abilities in consideration of mobility. So, many researchers use contact and/or non-contact methods to determine a terrain whether the vehicle can move on or not. In this paper we introduce an algorithm to classify terrains using visual information(one of the non-contacting methods). As a pre-processing, a contrast enhancement technique is introduced to improve classification of terrain. Also, for conducting classification algorithm, training images are grouped according to materials of the surface, and then Bayesian classification are applied to new images to determine membership to each group. In addition to the classification, we can build Traversability map specified by friction coefficients on which autonomous vehicles can decide to go or not. Experiments are made with Load-Cell to determine real friction coefficients of various terrains.

• Earthquakes and Structures
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• v.15 no.3
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• pp.263-273
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• 2018

Operational modal analysis is being widely used in aerospace, mechanical and civil engineering. Common research fields include optimal design and rehabilitation under dynamic loads, structural health monitoring, modification and control of dynamic response and analytical model updating. In many practical cases, influence of noise contamination in the recorded data makes it difficult to identify the modal parameters accurately. In this paper, an improved frequency domain method called Enhanced Least Square Complex Frequency (eLSCF) is developed to extract modal parameters from noisy recorded data. The proposed method makes the use of pre-defined approximate mode shape vectors to refine the cross-power spectral density matrix and extract fundamental frequency for the mode of interest. The efficiency of the proposed method is illustrated using an example five story shear frame loaded by random excitation and different noise signals.

• Journal of Computing Science and Engineering
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• v.5 no.4
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• pp.346-353
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• 2011

With the advent of ubiquitous computing, a massive amount of trajectory data has been published and shared in many websites. This type of computing also provides motivation for online mining of trajectory data, to fit user-specific preferences or context (e.g., time of the day). While many trajectory clustering algorithms have been proposed, they have typically focused on offline mining and do not consider the restrictions of the underlying road network and selection conditions representing user contexts. In clear contrast, we study an efficient clustering algorithm for Boolean + Clustering queries using a pre-materialized and summarized data structure. Our experimental results demonstrate the efficiency and effectiveness of our proposed method using real-life trajectory data.

• Proceedings of the KSR Conference
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• 2003.10b
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• pp.429-435
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• 2003

This paper presents the case studies of Tubular Roof construction Method(T.R.c.M) and Semi Shield method, which were applied to the tunnel excavation under the pre-existing railways. It was proved that T.R.c.M was an effective and safe method for the tunnel excavated in soft soil, giving little damage to the railways located a few meters above. Semi Shield was also performed successfully to bore a tunnel in soft and hard rock, minimizing the ground settlement and tilting of vulnerable fuel tanks. Site and soil conditions are also discussed, which led these relatively new methods to success. Finally, comparison of the measurement results and the design values are made to verify and improve the current design practice.

• Journal of computational fluids engineering
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• v.9 no.4
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• pp.7-12
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• 2004

In the present study a semi-automatic mesh generation program has been developed by using DXF file containing contour line data. The program consists of DXF file reader and mapping algorithm. Pre-generated 2-D planar mesh points are to be mapped one by one onto triangular surface whose three vertices are three nearest contour points surrounding the mapping point. The present program has been successfully tested for mesh generations for the road tunnel ventilation analysis and analysis of lava movement in mountain area.

• Proceedings of the KSR Conference
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• 2007.05a
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• pp.543-550
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• 2007

This study is for an effective response as the pre-stage for railway agencies to prepare the rail road kind based emergency response standard operation procedures when the emergency event occurs. Accordingly, I would like to increase the utility in helping the railroad employees to make their decisions. And that is possible through suggesting them to make computerized Emergency situation based activity and Emergency response that is individual based. And of course all of this procedure would be based on the emergency event categorized response scenario.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.1
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• pp.8-15
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• 2011

This paper presents an effective method for detecting vehicles in front of the camera-assisted car during nighttime driving. The proposed method detects vehicles based on detecting vehicle headlights and taillights using techniques of image segmentation and clustering. First, in order to effectively extract spotlight of interest, a pre-signal-processing process based on camera lens filter and labeling method is applied on road-scene images. Second, to spatial clustering vehicle of detecting lamps, a grouping process use light tracking method and locating vehicle lighting patterns. For simulation, we are implemented through Da-vinci 7437 DSP board with visible light mono-camera and tested it in urban and rural roads. Through the test, classification performances are above 89% of precision rate and 94% of recall rate evaluated on real-time environment.