• Journal of the Korean Society for Precision Engineering
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• v.16 no.10
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• pp.25-34
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• 1999

In this paper, an algorithm is presented to recognize lane and obstacles based on highway road image. The road images obtained by a video camera undergoes a pre-processing that includes filtering, edge detection, and identification of lanes. After this pre-processing, a part of image is grouped into 27 sub-windows and fed into a three-layer feed-forward neural network. The neural network is trained to indicate the road direction and the presence of absence of an obstacle. The proposed algorithm has been tested with the images different from the training images, and demonstrated its efficacy for recognizing lane and obstacles. Based on the test results, it can be said that the algorithm successfully combines the traditional image processing and the neural network principles towards a simpler and more efficient driver warning of assistance system

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.23-26
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• 2000

An improved inverse perspective mapping (IIPM) is proposed so as to reduce computational expense of recovery of 3D road surface. An experimental system based on IIPM is developed to detect lane parameters for a driver assistant system. A re-organized image is obtained quickly and exactly by IIPM. Efficient preprocessing techniques are used to enhance the information of lane and obstacles. Lane in the preprocessed. image is located with region identification. Lane parameters are estimated effectively. An algorithm to adaptively modify the parameters of IIPM is given. Properties of obstacle on 3D road surface are discussed and used to detect obstacles in the current lane and neighboring lanes. Experimental results show that the new method can extract lane state information effectively.

• Asian Journal of Atmospheric Environment
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• v.8 no.3
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• pp.115-125
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• 2014

In this study, particle composition data for $PM_{10}$ samples were collected every 3 days at Seoul, Korea from August 2006 to November 2007, and were analyzed to provide source identification and apportionment. A total of 164 samples were collected and 21 species (15 inorganic species, 4 ionic species, OC, and EC) were analyzed by particle-induced x-ray emission, ion chromatography, and thermal optical transmittance methods. Positive matrix factorization (PMF) was used to develop source profiles and to estimate their mass contributions. The PMF modeling identified nine sources and the average mass was apportioned to secondary nitrate (9.3%), motor vehicle (16.6%), road salt (5.8%), industry (4.9%), airborne soil (17.2 %), aged sea salt (6.2%), field burning (6.0%), secondary sulfate (16.2%), and road dust (17.7%), respectively. The nonparametric regression (NPR) analysis was used to help identify local source in the vicinity of the sampling area. These results suggest the possible strategy to maintain and manage the ambient air quality of Seoul.

• Structural Monitoring and Maintenance
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• v.7 no.4
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• pp.283-294
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• 2020

In this paper, an adaptive MEMD based modal identification technique for linear time-invariant systems is proposed employing multiple vibration measurements. Traditional empirical mode decomposition (EMD) suffers from mode-mixing during sifting operations to identify intrinsic mode functions (IMF). MEMD performs better in this context as it considers multi-channel data and projects them into a n-dimensional hypercube to evaluate the IMFs. Using this technique, modal parameters of the structural system are identified. It is observed that MEMD has superior performance compared to its traditional counterpart. However, it still suffers from mild mode-mixing in higher modes where the energy contents are low. To avoid this problem, an adaptive filtering scheme is proposed to decompose the interfering modes. The Proposed modified scheme is then applied to vibrations of a reinforced concrete road bridge. Results presented in this study show that the proposed MEMD based approach coupled with the filtering technique can effectively identify the parameters of the dominant modes present in the structural response with a significant level of accuracy.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.1205-1214
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• 2020

The poverty level in Buru Regency is still high, despite the relatively stable economic growth. For this reason, the purpose of this study was to (1) Identify the leading commodity in each district in Buru Regency; (2) Analyze the effect of road infrastructure and leading commodities on poverty. The findings show that the most sparsely populated district is Fena Leisela, with mangoes as the leading commodity. Pineapple, langsat, apple rose, cabbages, cashews, coffee, cashew, melon, and watermelon are the leading products in Air Buaya, Batabual, Waplau, Lolong Guba, Lilialy, Waelata, Namlea, Kaiely Bay, and Waeapo, respectively. Additionally, the results also indicate that road infrastructure and leading commodities have a significant effect on poverty alleviation in Buru Regency. It means that improving infrastructure and increasing leading commodities production reduce poverty in the region. Good road infrastructure can promote connectivity between regions so that it can accelerate and expand economic development. The provision of infrastructure that encourages connectivity will reduce transportation costs and logistics costs to increase product competitiveness and accelerate the economic movement. When the road infrastructure in Buru Regency improves and new roads are built, it can improve transportation access, it will reduce the living cost for the poor and increase income, and open up opportunities for the poor to benefit from economic growth.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.41-60
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• 2018

Recently, road traffic congestion is becoming a serious urban phenomenon, leading to massive adverse impacts on the ecology and economy. Therefore, solving this problem has drawn public attention throughout the world. One new promising solution is to take full advantage of vehicular ad hoc networks (VANETs). In this study, we propose a new traffic congestion detection and quantification method based on vehicle clustering and fuzzy assessment in VANET environment. To enhance real-time performance, this method collects traffic information by vehicle clustering. The average speed, road density, and average stop delay are selected as the characteristic parameters for traffic state identification. We use a comprehensive fuzzy assessment based on the three indicators to determine the road congestion condition. Simulation results show that the proposed method can precisely reflect the road condition and is more accurate and stable compared to existing algorithms.

• Advances in nano research
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• v.16 no.6
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• pp.531-548
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• 2024

The study focuses on using remote sensing to gather data about the Earth's surface, particularly in urban environments, using satellites and aircraft-mounted sensors. It aims to develop a classification framework for road targets using multi-spectral imagery. By integrating Convolutional Neural Networks (CNNs) with XGBoost, the study seeks to enhance the accuracy and efficiency of road target identification, aiding urban infrastructure management and transportation planning. A novel aspect of the research is the incorporation of quantum sensors, which improve the resolution and sensitivity of the data. The model achieved high predictive accuracy with an MSE of 0.025, R-squared of 0.85, RMSE of 0.158, and MAE of 0.12. The CNN model showed excellent performance in road detection with 92% accuracy, 88% precision, 90% recall, and an f1-score of 89%. These results demonstrate the model's robustness and applicability in real-world urban planning scenarios, further enhanced by data augmentation and early stopping techniques.

• Journal of Auto-vehicle Safety Association
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• v.7 no.2
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• pp.15-18
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• 2015

The availability of in-depth accident data is a prerequisite for each efficient traffic safety management system. Identification and definition of the relevant problem together with knowledge of the data and parameters describing this problem is essential for its successful solution. Comprehensive, up-to-date, accident data is needed for recognition of the scope of road safety problems and for raising public awareness. Reliable and relevant data enable the identification of the contributory factors of the individual accidents, and an unveiling of the background of the risk behaviour of the road users. It offers the best way to explore the prevention of accidents, and ways to implement measures to reduce accident severity. In this study, reviewing the existing iGlad and GIDAS system, KIDAS data format can be finalized through feasibility evaluation. The progressive approach is proposed to successful settlement of Korea in-depth accident study. As the initial stage of in-depth investigation DB construction, the KIDAS is not repetition of the current police based TAAS. It is essential part of improving vehicle safety and reduction of traffic fatality in Korea. 72 Contributing factors like road and traffic characteristics, vehicle parameters, and information about the people involved in the accident have to be investigated and registered as well in the KIDAS.

• Journal of KIISE
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• v.43 no.2
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• pp.186-195
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• 2016

This paper proposes a vehicle license plate detection method in real road environments using 8 bit-MCT features and a landmark-based Adaboost method. The proposed method allows identification of the potential license plate region, and generates a saliency map that presents the license plate's location probability based on the Adaboost classification score. The candidate regions whose scores are higher than the given threshold are chosen from the saliency map. Each candidate region is adjusted by the local image variance and verified by the SVM and the histograms of the 8bit-MCT features. The proposed method achieves a detection accuracy of 85% from various road images in Korea and Europe.

• The Journal of Economics, Marketing and Management
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• v.5 no.1
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• pp.38-43
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• 2017

Purpose - The paper aims to study competency mapping in the organization and how does HR department focuses to provide a definitive road map to understand, design and implement competency models in the organization. Research Design, Data and Methodology - Descriptive research design has been used in the study. The sample size of the study is 75 consisting of employees working at DMRC, barakhamba road. A structured questionnaire was designed to collect the primary data. SPSS has been used to analyze the responses of the questionnaires. Results- DMRC frequently employs some form of competency mapping to understand how to most effectively employ the competencies of strengths of workers. Conclusion - To conclude, it was found that the company has cost effective system to recruit and select people which is working satisfactorily.