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Статті в журналах з теми "GAUSSIAN BASED HIGHWAY"
Xu, Hong Ke, Chao Cai, Hao Chen, Jian Wu Fang, and Shu Guang Li. "Research on License Plate Tracking and Detection Based on Optical Flow." Applied Mechanics and Materials 135-136 (October 2011): 775–80. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.775.
Повний текст джерелаAbdelkader, Eslam Mohammed, Abobakr Al-Sakkaf, Nehal Elshaboury, and Ghasan Alfalah. "Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components." Processes 10, no. 1 (December 24, 2021): 36. http://dx.doi.org/10.3390/pr10010036.
Повний текст джерелаTohti, Gulbahar, Mamtimin Gheni, Yu Feng Chen, and Mamatjan Tursun. "Capacity Analysis of Urban Highway Intersections." Key Engineering Materials 462-463 (January 2011): 1170–75. http://dx.doi.org/10.4028/www.scientific.net/kem.462-463.1170.
Повний текст джерелаZhu, Zixuan, Chenglong Teng, Yingfeng Cai, Long Chen, Yubo Lian, and Hai Wang. "Vehicle Safety Planning Control Method Based on Variable Gauss Safety Field." World Electric Vehicle Journal 13, no. 11 (October 31, 2022): 203. http://dx.doi.org/10.3390/wevj13110203.
Повний текст джерелаPeng, Tao, Li-li Su, Zhi-wei Guan, Hai-jing Hou, Jun-kai Li, Xing-liang Liu, and Yi-ke Tong. "Lane-Change Model and Tracking Control for Autonomous Vehicles on Curved Highway Sections in Rainy Weather." Journal of Advanced Transportation 2020 (November 25, 2020): 1–15. http://dx.doi.org/10.1155/2020/8838878.
Повний текст джерелаZhang, Yule, and Shoulin Zhu. "Study on the Effect of Driving Time on Fatigue of Grassland Road Based on EEG." Journal of Healthcare Engineering 2021 (July 8, 2021): 1–9. http://dx.doi.org/10.1155/2021/9957828.
Повний текст джерелаAghayari, M., P. Pahlavani, and B. Bigdeli. "A GEOGRAPHIC WEIGHTED REGRESSION FOR RURAL HIGHWAYS CRASHES MODELLING USING THE GAUSSIAN AND TRICUBE KERNELS: A CASE STUDY OF USA RURAL HIGHWAYS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 27, 2017): 305–9. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-305-2017.
Повний текст джерелаOguri, Y., T. Yamashita, A. Ebihara, N. Kambe, and J. Hasegawa. "PIXE MEASUREMENT OF ATMOSPHERIC PARTICULATE MATTER IN A RESIDENTIAL AREA NEAR A MAJOR URBAN HIGHWAY." International Journal of PIXE 10, no. 03n04 (January 2000): 127–35. http://dx.doi.org/10.1142/s0129083500000183.
Повний текст джерелаKorablev, R. A., V. P. Belokurov, and S. V. Belokurov. "Influence of anthropogenic impact of vehicles on roadside forest plantations." IOP Conference Series: Earth and Environmental Science 875, no. 1 (October 1, 2021): 012079. http://dx.doi.org/10.1088/1755-1315/875/1/012079.
Повний текст джерелаQing, Feng, Yan Zhao, Xingmin Meng, Xiaojun Su, Tianjun Qi, and Dongxia Yue. "Application of Machine Learning to Debris Flow Susceptibility Mapping along the China–Pakistan Karakoram Highway." Remote Sensing 12, no. 18 (September 10, 2020): 2933. http://dx.doi.org/10.3390/rs12182933.
Повний текст джерелаДисертації з теми "GAUSSIAN BASED HIGHWAY"
Zhang, Lin. "Semiparametric Bayesian Kernel Survival Model for Highly Correlated High-Dimensional Data." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/95040.
Повний текст джерелаPHD
AGGARWAL, ANCHAL. "SENSITIVE ANALYSIS OF CALINE 4 HIGHWAY DISPERSION MODEL UNVER MIXED TRAFIC CONDITIONS." Thesis, 2012. http://dspace.dtu.ac.in:8080/jspui/handle/repository/13911.
Повний текст джерелаRapid urbanization and industrialization of cities have increased the vehicular traffic leading to increase in air pollution in urban areas. It has been estimated that in India road traffic contributes approximately 70% of air pollution in urban areas. To reduce the impacts of air pollution due to vehicular traffic, it is important to manage and improve the quality of air in such urban areas. Air pollution dispersion models are used to effectively and efficiently plan the management (environment management plan) of vehicular traffic pollution on particular area/ road corridor, along with monitoring of air pollutants. They not only aid in determining the present influenced area/ affected due to vehicular traffic pollution but also help in identifying the future scenarios under different traffic and meteorological conditions made by these models. Vehicular pollution modeling involves air pollution prediction estimates by simulating impact of emissions from vehicular activities in a given region under specified traffic and meteorological conditions. Throughout the world, including India the prediction of vehicular pollutant concentrations along highways and roads are carried out by using various Gaussian-based highway dispersion models. Based on the Gaussian dispersion model, several prediction models have been developed to predict vehicular pollution levels along the highways. The most popular amongst various highway dispersion models, are the CALINE model (latest being CALINE 4). CALINE 4 developed by Benson (1984) is extensively used throughout the world (including India) for various vehicular pollution estimate/ prediction along the highways. The CALINE 4 Model uses various inputs (viz., Traffic Volume, Emission Factor, Road geometry, Wind Speed, Wind Direction, Background Concentration) to predict the air pollution concentrations at pre-identified receptor locations along the highway. The present study focuses on sensitivity analysis of CALINE-4 model which is the fourth version simple line source Gaussian plume dispersion model. Ashram Chowk – CRRI highway Corridor of NH-2 was selected as the area of study. Inputs data (viz. traffic volume, traffic compositions, meteorological data etc.) required for CALINE 4 model was collected from field surveys data. Emission factors provided by CPCB (2000) and ARAI (2007) were used to estimate Weighted Emission Factor (WEF) to account for mixed traffic conditions. The CO concentration due to traffic along the xiii Ashram Chowk – CRRI highway corridor was predicted at the pre-identified receptor locations. The dispersion of CO concentrations was found to be present upto a distance of 150m from the edge of the mixing zone width (road width+3m on each side of the road). The predicted CO concentrations in all the cases (viz., 1-hour Standard Case Run Conditions, 1-hour Worse Case Run Conditions) were within the National Ambient Air Quality Standard, 2009 (NAAQS, 2009) (i.e. 2 mg/m3 for 8 hours and 4 mg/m3 for 1 hour for CO). The regression coefficient (r2) between predicted and observed 1-hour CO concentrations using CPCB emission factors for Standard Case Run Condition was 0.65 and for Worse Case Run Condition was 0.76. Similarly, the regression coefficient (r2) between predicted and observed 1-hour CO concentrations using ARAI emission factors for Standard Case Run Condition was 0.60 and for Worse Case Run Condition was 0.73. A sensitivity analysis of the CALINE 4 model had been performed to identify the most influential variables. CALINE 4 model was found to be relatively sensitive to wind angle (s) for small receptor distances. The highest CO concentrations were observed by a wind angle of ~10° as measured from the road centerline. Wind speed had a considerable effect, e.g., predicted CO concentrations were dropped by 75% - 80% as wind speed increased from 0.5 to 5 m/s. From unstable to stable conditions, average increase in CO concentration was 43%. The model consistently predicts lower CO concentrations for greater highway widths. This effect was most apparent for receptors near the roadway edge. Roadway height (from receptor location at ground level) had very less effect for small change in height but has considerable effect for more deeper or elevated roadway height. Sensitive Analysis of CALINE 4 had also revealed that among various input variable, source strength, wind speed, highway width and median width were most significant input variable and wind direction, roadway height, distance of receptor to roadway and atmospheric stability were the less significant input variables. Surface Roughness and Mixing height had negligible effect on predicted CO concentrations.
Частини книг з теми "GAUSSIAN BASED HIGHWAY"
Scott, Jennifer, and Miroslav Tůma. "Sparse Matrix Ordering Algorithms." In Nečas Center Series, 135–61. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-25820-6_8.
Повний текст джерелаLittle, Max A. "Linear-Gaussian systems and signal processing." In Machine Learning for Signal Processing, 187–264. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198714934.003.0007.
Повний текст джерелаPathak, Vishwambhar. "Autonomous Market Segments Estimation Using Density Conscious Artificial Immune System Learner." In Advances in Business Information Systems and Analytics, 110–35. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2234-8.ch006.
Повний текст джерелаZhu, Meng, and Atta Badii. "Cross-Modal Semantic-Associative Labelling, Indexing and Retrieval of Multimodal Data." In Multiple Sensorial Media Advances and Applications, 234–57. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-60960-821-7.ch012.
Повний текст джерелаErman, Burak, and James E. Mark. "Overview and Some Fundamental Information." In Structures and Properties of Rubberlike Networks. Oxford University Press, 1997. http://dx.doi.org/10.1093/oso/9780195082371.003.0003.
Повний текст джерелаТези доповідей конференцій з теми "GAUSSIAN BASED HIGHWAY"
Zhang, Mingheng, Zhengxian Guo, Zhaoyang Liu, and Xing Wan. "Research of Driving Fatigue Detection Based on Gaussian Mixture Hidden Markov Model." In 3rd International Forum on Connected Automated Vehicle Highway System through the China Highway & Transportation Society. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2020. http://dx.doi.org/10.4271/2020-01-5158.
Повний текст джерелаLiu, Wenjun, Yulin Zhai, Guang Chen, and Alois Knoll. "Gaussian Process based Model Predictive Control for Overtaking Scenarios at Highway Curves." In 2022 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2022. http://dx.doi.org/10.1109/iv51971.2022.9827233.
Повний текст джерелаShinn, Tyler, Richard Carpenter, and Roger C. Fales. "State Estimation Techniques for Axial Piston Pump Health Monitoring." In ASME/BATH 2015 Symposium on Fluid Power and Motion Control. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/fpmc2015-9621.
Повний текст джерелаMamann, Hadriel, Thomas Nieddu, Mathieu Bozzio, Félix Hoffet, Félix Garreau de Loubresse, Eleni Diamanti, Alban Urvoy, and Julien Laurat. "Quantum cryptographic protocol implementation using a highly-efficient cold-atom-based quantum memory." In CLEO: Fundamental Science. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/cleo_fs.2023.ff2a.3.
Повний текст джерелаLi, Meng, Mohammad Kazem Sadoughi, Zhen Hu, and Chao Hu. "System Reliability Analysis Using Hybrid Gaussian Process Model." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-98173.
Повний текст джерелаLeahu, Haralambie, Michael Kaisers, and Tim Baarslag. "Automated Negotiation with Gaussian Process-based Utility Models." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/60.
Повний текст джерелаZhao, Wentian, Shaojie Wang, Zhihuai Xie, Jing Shi, and Chenliang Xu. "GAN-EM: GAN Based EM Learning Framework." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/612.
Повний текст джерелаGuo, Tuan, Jacques Albert, Chengkun Chen, Alexei Ivanov, and Albane Laronche. "Highly accurate micro-displacement measurement based on Gaussian-chirped tilted fiber Bragg grating." In 19th International Conference on Optical Fibre Sensors, edited by David D. Sampson. SPIE, 2008. http://dx.doi.org/10.1117/12.785637.
Повний текст джерелаLi, Guang, and J. Quincy Brown. "Optimizing Imaging Throughput and Resolution in Light Sheet Microscopy Using Deep-learning-based Beam Shape Translation." In Novel Techniques in Microscopy. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/ntm.2023.ntu1c.3.
Повний текст джерелаLin, Po Ting, Wei-Hao Lu, and Shu-Ping Lin. "A Comprehensive Investigation of Ensembles of Gaussian-Based and Gradient-Based Transformed Reliability Analyses: When and How to Use Them." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59151.
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