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Artykuły w czasopismach na temat "GAUSSIAN BASED HIGHWAY"
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Xu, Hong Ke, Chao Cai, Hao Chen, Jian Wu Fang i Shu Guang Li. "Research on License Plate Tracking and Detection Based on Optical Flow". Applied Mechanics and Materials 135-136 (październik 2011): 775–80. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.775.
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Aiming at regulating the toll evasion behaviors in highway weight charges and reducing charge disputes caused by jumping, this article studied the algorithm that tracks vehicle beating when it is passing the scale. Based on license plate location, vehicle movement could be characterized by tracking the plate centroid using Lucas-Kanade optical flow algorithm. The optical flow vector of the centroid was calculated frame by frame, which could be used for drawing trajectory of centroid coordinates, and calculating beating parameters. In order to expand the detection range and adaptability of the algorithm, through calculating optical flow hierarchically combined with Gaussian pyramid, then tracking centroid from high lever to low in the image pyramid, it could achieve the capture of the vehicle' fast moving. Through experiments, trajectory reflected vehicle beating information well, which provides a strong evidence means for levy problem of the highway weight charges.
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Abdelkader, Eslam Mohammed, Abobakr Al-Sakkaf, Nehal Elshaboury i Ghasan Alfalah. "Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components". Processes 10, nr 1 (24.12.2021): 36. http://dx.doi.org/10.3390/pr10010036.
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Highway tunnels are one of the paramount infrastructure systems that affect the welfare of communities. They are vulnerable to higher limits of deterioration, yet there are limited available funds for maintenance and rehabilitation. This state of circumstances entails the development of a deterioration model to forecast the performance condition behavior of critical tunnel elements. Accordingly, this research paper proposes an integrated deterioration prediction model for five highway tunnel elements, namely, cast-in-place tunnel liners, concrete interior walls, concrete portal, concrete ceiling slab, and concrete slab on grade. The developed deterioration model is envisioned in two fundamental components, which are model calibration and model assessment. In the first component, an integrated model of Gaussian process regression and a grey wolf optimization algorithm (GWO-GPR) is introduced for deterioration behavior prediction of highway tunnel elements. In this regard, the grey wolf optimizer is exploited to improve the prediction accuracies of the Gaussian process through optimal estimation of its hyper parameters and to automatically interpret the significant deterioration factors. The second component involves three tiers of performance evaluation comparison, statistical significance comparisons, and consolidated ranking to assess the prediction accuracies of the developed GWO-GPR model. In this regard, the developed model is validated against six widely acknowledged machine learning models, which are back-propagation artificial neural network, Elman neural network, cascade forward neural network, generalized regression neural network, support vector machines, and regression tree. Results demonstrate that the developed GWO-GPR model significantly outperformed other deterioration prediction models in the five tunnel elements. In cast-in-place tunnel liners it accomplished a mean absolute percentage error, mean absolute error, root mean square percentage error, root relative squared error, and relative absolute error of 1.65%, 0.018, 0.21%, 0.018, and 0.147, respectively. In this context, it was inferred that the developed GWO-GPR model managed to reduce the prediction errors of the back-propagation artificial neural network, Elman neural network, and support vector machines by 84.71%, 76.91%, and 69.6%, respectively. It can be concluded that the developed deterioration model can assist transportation agencies in creating timely and cost-efficient maintenance schedules of highway tunnels.
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Tohti, Gulbahar, Mamtimin Gheni, Yu Feng Chen i Mamatjan Tursun. "Capacity Analysis of Urban Highway Intersections". Key Engineering Materials 462-463 (styczeń 2011): 1170–75. http://dx.doi.org/10.4028/www.scientific.net/kem.462-463.1170.
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In this paper, based on survey data of delays on intersections in urban highways and in accordance with theoretical capacity of each traffic lane, various reasons of delay in intersections are analyzed numerically. A discrete traffic model to simulate traffic in intersections using Gaussian mesh method is built. After modifying intersection properties, weight of each factor in terms of their effect to capacity is acquired. An optimized way to solve traffic delay is hereby recommended.
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Zhu, Zixuan, Chenglong Teng, Yingfeng Cai, Long Chen, Yubo Lian i Hai Wang. "Vehicle Safety Planning Control Method Based on Variable Gauss Safety Field". World Electric Vehicle Journal 13, nr 11 (31.10.2022): 203. http://dx.doi.org/10.3390/wevj13110203.
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The existing intelligent vehicle trajectory-planning methods have limitations in terms of efficiency and safety. To overcome these limitations, this paper proposes an automatic driving trajectory-planning method based on a variable Gaussian safety field. Firstly, the time series bird’s-eye view is used as the input state quantity of the network, which improves the effectiveness of the trajectory planning policy network in extracting the features of the surrounding traffic environment. Then, the policy gradient algorithm is used to generate the planned trajectory of the autonomous vehicle, which improves the planning efficiency. The variable Gaussian safety field is used as the reward function of the trajectory planning part and the evaluation index of the control part, which improves the safety of the reinforcement learning vehicle tracking algorithm. The proposed algorithm is verified using the simulator. The obtained results show that the proposed algorithm has excellent trajectory planning ability in the highway scene and can achieve high safety and high precision tracking control.
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Peng, Tao, Li-li Su, Zhi-wei Guan, Hai-jing Hou, Jun-kai Li, Xing-liang Liu i Yi-ke Tong. "Lane-Change Model and Tracking Control for Autonomous Vehicles on Curved Highway Sections in Rainy Weather". Journal of Advanced Transportation 2020 (25.11.2020): 1–15. http://dx.doi.org/10.1155/2020/8838878.
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In this study, we propose an adaptive path planning model and tracking control method for collision avoidance and lane-changing manoeuvres on highways in rainy weather. Considering the human-vehicle-road interaction, we developed an adaptive lane change system that consists of an intelligent trajectory planning and tracking controller. Gaussian distribution was introduced to evaluate the impact of rain on the pavement characteristics and deduce adaptive lane-change trajectories. Subsequently, a score-based decision mechanism and multilevel autonomous driving mode that considers safety, comfort, and efficiency were proposed. A tracking controller was designed using a linearised model predictive control method. Finally, using simulated scenarios, the feasibility and effectiveness of the proposed method were demonstrated. The results obtained herein are a valuable resource that can be used to develop an intelligent lane change system for autonomous vehicles and can help improve highway traffic safety and efficiency in adverse weather conditions.
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Zhang, Yule, i Shoulin Zhu. "Study on the Effect of Driving Time on Fatigue of Grassland Road Based on EEG". Journal of Healthcare Engineering 2021 (8.07.2021): 1–9. http://dx.doi.org/10.1155/2021/9957828.
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In order to study the change law of the fatigue degree of grassland expressway drivers over time, this paper takes the semidesert grassland landscape of Xilinhot city as the experimental environment and takes the provincial highway S101 (K278–K424) as an example to design an actual driving test. Taking Urumqi, Inner Mongolia Autonomous Region, as the experimental section, combined with the Biopac MP150 multichannel physiological instrument and its auxiliary knowledge software and mathematical statistics methods, the relationship between EEG and time was studied. The test results show that the primary fatigue factor F1 and the secondary fatigue factor F2 can summarize the fatigue law characterized by 96.42% of EEG information. During 130 minutes of driving on the prairie highway, the periods of high fatigue were 105 minutes and 125 minutes, respectively. Driving fatigue can be divided into three stages over time: 5–65 min fatigue-free stage, 70–85 min fatigue transition stage, and 90–130 min fatigue stage. Fatigue changes over time. The law follows the Gaussian function and the sine function.
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Aghayari, M., P. Pahlavani i 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 (27.09.2017): 305–9. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-305-2017.
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Based on world health organization (WHO) report, driving incidents are counted as one of the eight initial reasons for death in the world. The purpose of this paper is to develop a method for regression on effective parameters of highway crashes. In the traditional methods, it was assumed that the data are completely independent and environment is homogenous while the crashes are spatial events which are occurring in geographic space and crashes have spatial data. Spatial data have spatial features such as spatial autocorrelation and spatial non-stationarity in a way working with them is going to be a bit difficult. The proposed method has implemented on a set of records of fatal crashes that have been occurred in highways connecting eight east states of US. This data have been recorded between the years 2007 and 2009. In this study, we have used GWR method with two Gaussian and Tricube kernels. The Number of casualties has been considered as dependent variable and number of persons in crash, road alignment, number of lanes, pavement type, surface condition, road fence, light condition, vehicle type, weather, drunk driver, speed limitation, harmful event, road profile, and junction type have been considered as explanatory variables according to previous studies in using GWR method. We have compered the results of implementation with OLS method. Results showed that R<sup>2</sup> for OLS method is 0.0654 and for the proposed method is 0.9196 that implies the proposed GWR is better method for regression in rural highway crashes.
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Oguri, Y., T. Yamashita, A. Ebihara, N. Kambe i J. Hasegawa. "PIXE MEASUREMENT OF ATMOSPHERIC PARTICULATE MATTER IN A RESIDENTIAL AREA NEAR A MAJOR URBAN HIGHWAY". International Journal of PIXE 10, nr 03n04 (styczeń 2000): 127–35. http://dx.doi.org/10.1142/s0129083500000183.
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In order to study influence of automobile traffic on a local urban atmospheric environment, we have investigated suspended particulate matter (SPM) collected at sampling sites in a region which includes a major highway and a residential area in the southern part of Tokyo during August - November 1999. The atomic composition of each sample was measured by means of PIXE analysis using a 2.0 MeV proton beam. Sixteen elements were quantitatively measured. The positional dependence of SPM loading was determined for each element using samples simultaneously collected at three different sites. For the experimental results obtained for downwind conditions, the measured concentration as a function of the distance from the highway was compared with a simple calculation based on the Gaussian plume model. The concentration distribution of some heavy elements in the fine fraction is well reproduced by this analysis. It has been found that for ordinary moderate downwind conditions the area within 300-400 m from the highway is directly affected by emission due to the automobile traffic.
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Korablev, R. A., V. P. Belokurov i S. V. Belokurov. "Influence of anthropogenic impact of vehicles on roadside forest plantations". IOP Conference Series: Earth and Environmental Science 875, nr 1 (1.10.2021): 012079. http://dx.doi.org/10.1088/1755-1315/875/1/012079.
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Abstract The article presents studies of the growth and development of forest stands along highways as a result of man-made impacts from road transport emissions. The obtained mathematical model describing the dynamics of the growth of the biomass of stands of various bonities of roadside stands during the period of light saturation is presented. In this regard, the obtained mathematical model describing the dynamics of the growth of the biomass of stands of various bonitets of roadside forest stands during the period of light saturation is presented. The use of the bonus in research to characterize the growth rate of forest roadside plantings depending on the distance to highways and the density of traffic flows on them allows us to characterize the amount of toxic pollutants entering forests. This allows us to assess the process of expanding the environmentally unfavorable zone along the highway. The article presents the possibility of calculating the concentration of pollutants, based on the model of turbulent diffusion, reduced, after some assumption, to the model of Gaussian distribution in atmospheric air. The dependence on the calculation of the intensity of emissions of pollutants, taking into account the composition of the traffic flow, is given.
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Qing, Feng, Yan Zhao, Xingmin Meng, Xiaojun Su, Tianjun Qi i Dongxia Yue. "Application of Machine Learning to Debris Flow Susceptibility Mapping along the China–Pakistan Karakoram Highway". Remote Sensing 12, nr 18 (10.09.2020): 2933. http://dx.doi.org/10.3390/rs12182933.
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The China–Pakistan Karakoram Highway is an important land route from China to South Asia and the Middle East via Pakistan. Due to the extremely hazardous geological environment around the highway, landslides, debris flows, collapses, and subsidence are frequent. Among them, debris flows are one of the most serious geological hazards on the Karakoram Highway, and they often cause interruptions to traffic and casualties. Therefore, the development of debris flow susceptibility mapping along the highway can potentially facilitate its safe operation. In this study, we used remote sensing, GIS, and machine learning techniques to map debris flow susceptibility along the Karakoram Highway in areas where observation data are scarce and difficult to obtain by field survey. First, the distribution of 544 catchments which are prone to debris flow were identified through visual interpretation of remote sensing images. The factors influencing debris flow susceptibility were then analyzed, and a total of 17 parameters related to geomorphology, soil materials, and triggering conditions were selected. Model training was based on multiple common machine learning methods, including Ensemble Methods, Gaussian Processes, Generalized Linear models, Navies Bayes, Nearest Neighbors, Support Vector Machines, Trees, Discriminant Analysis, and eXtreme Gradient Boosting. Support Vector Classification (SVC) was chosen as the final model after evaluation; its accuracy (ACC) was 0.91, and the area under the ROC curve (AUC) was 0.96. Among the factors involved in SVC, the Melton Ratio (MR) was the most important, followed by drainage density (DD), Hypsometric Integral (HI), and average slope (AS), indicating that geomorphic conditions play an important role in predicting debris flow susceptibility in the study area. SVC was used to map debris flow susceptibility in the study area, and the results will potentially facilitate the safe operation of the highway.
Rozprawy doktorskie na temat "GAUSSIAN BASED HIGHWAY"
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Zhang, Lin. "Semiparametric Bayesian Kernel Survival Model for Highly Correlated High-Dimensional Data". Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/95040.
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We are living in an era in which many mysteries related to science, technologies and design can be answered by "learning" the huge amount of data accumulated over the past few decades. In the processes of those endeavors, highly-correlated high-dimensional data are frequently observed in many areas including predicting shelf life, controlling manufacturing processes, and identifying important pathways related with diseases. We define a "set" as a group of highly-correlated high-dimensional (HCHD) variables that possess a certain practical meaning or control a certain process, and define an "element" as one of the HCHD variables within a certain set. Such an elements-within-a-set structure is very complicated because: (i) the dimensions of elements in different sets can vary dramatically, ranging from two to hundreds or even thousands; (ii) the true relationships, include element-wise associations, set-wise interactions, and element-set interactions, are unknown; (iii) and the sample size (n) is usually much smaller than the dimension of the elements (p). The goal of this dissertation is to provide a systematic way to identify both the set effects and the element effects associated with survival outcomes from heterogeneous populations using Bayesian survival kernel models. By connecting kernel machines with semiparametric Bayesian hierarchical models, the proposed unified model frameworks can identify significant elements as well as sets regardless of mis-specifications of distributions or kernels. The proposed methods can potentially be applied to a vast range of fields to solve real-world problems.PHD
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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.
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M.TECHRapid 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.
Części książek na temat "GAUSSIAN BASED HIGHWAY"
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Scott, Jennifer, i Miroslav Tůma. "Sparse Matrix Ordering Algorithms". W Nečas Center Series, 135–61. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-25820-6_8.
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AbstractSo far, our focus has been on the theoretical and algorithmic principles involved in sparse Gaussian elimination-based factorizations. To limit the storage and the work involved in the computation of the factors and in their use during the solve phase it is generally necessary to reorder (permute) the matrix before the factorization commences. The complexity of the most critical steps in the factorization is highly dependent on the amount of fill-in, as can be seen from the following observation.
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Little, Max A. "Linear-Gaussian systems and signal processing". W Machine Learning for Signal Processing, 187–264. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198714934.003.0007.
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Linear systems theory, based on the mathematics of vector spaces, is the backbone of all “classical” DSP and a large part of statistical machine learning. The basic idea -- that linear algebra applied to a signal can of substantial practical value -- has counterparts in many areas of science and technology. In other areas of science and engineering, linear algebra is often justified by the fact that it is often an excellent model for real-world systems. For example, in acoustics the theory of (linear) wave propagation emerges from the concept of linearization of small pressure disturbances about the equilibrium pressure in classical fluid dynamics. Similarly, the theory of electromagnetic waves is also linear. Except when a signal emerges from a justifiably linear system, in DSP and machine learning we do not have any particular correspondence to reality to back up the choice of linearity. However, the mathematics of vector spaces, particularly when applied to systems which are time-invariant and jointly Gaussian, is highly tractable, elegant and immensely useful.
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Pathak, Vishwambhar. "Autonomous Market Segments Estimation Using Density Conscious Artificial Immune System Learner". W Advances in Business Information Systems and Analytics, 110–35. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2234-8.ch006.
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Automated exploration of groups of customers to understand customer behavior from raw data is highly required to support strategic decision making given the pressure of competitive market. Several mathematical and statistical methods have been applied for autonomous model estimation from multivariate data. The current paper investigates employability of new generation of bio-inspired metaheuristic algorithms, named the artificial immune system (AIS), which in the current proposition, learn through density based kernels. As such the model simulates probabilistic behavior of the dendritic cells (DCs) during recognition of the antigens and danger signals, whose learning has been modeled with an infinite Gaussian mixture model. The unsupervised learning capability of the model has been found to be effective for multivariate data.
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Zhu, Meng, i Atta Badii. "Cross-Modal Semantic-Associative Labelling, Indexing and Retrieval of Multimodal Data". W Multiple Sensorial Media Advances and Applications, 234–57. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-60960-821-7.ch012.
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Digitalised multimedia information today is typically represented in different modalities and distributed through various channels. The use of such a huge amount of data is highly dependent on effective and efficient cross-modal labelling, indexing and retrieval of multimodal information. In this Chapter, we mainly focus on the combining of the primary and collateral modalities of the information resource in an intelligent and effective way in order to provide better multimodal information understanding, classification, labelling and retrieval. Image and text are the two modalities we mainly talk about here. A novel framework for semantic-based collaterally cued image labelling had been proposed and implemented, aiming to automatically assign linguistic keywords to regions of interest in an image. A visual vocabulary was constructed based on manually labelled image segments. We use Euclidean distance and Gaussian distribution to map the low-level region-based image features to the high-level visual concepts defined in the visual vocabulary. Both the collateral content and context knowledge were extracted from the collateral textual modality to bias the mapping process. A semantic-based high-level image feature vector model was constructed based on the labelling results, and the performance of image retrieval using this feature vector model appears to outperform both content-based and text-based approaches in terms of its capability for combining both perceptual and conceptual similarity of the image content.
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Erman, Burak, i James E. Mark. "Overview and Some Fundamental Information". W Structures and Properties of Rubberlike Networks. Oxford University Press, 1997. http://dx.doi.org/10.1093/oso/9780195082371.003.0003.
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This chapter is a brief overview of the topics treated in the book. It is aimed, in particular, at providing some qualitative information on rubber elasticity theories and their relationships to experimental studies, and at putting this material into context. The following chapter describes in detail the classical theories of rubber elasticity, that is, the phantom and affine network theories. The network chains in the phantom model are assumed not to experience the effects of the surrounding chains and entanglements, and thus to move as “phantoms.” Although this seems to be a very severe approximation, many experimental results are not in startling disagreement with theories based on this highly idealized assumption. These theories associate the total Helmholtz free energy of a deformed network with the sum of the free energies of the individual chains—an important assumption adopted throughout the book. They treat the single chain in its maximum simplicity, as a Gaussian chain, which is a type of “structureless” chain (where the only chemical constitution specified is the number of bonds in the network chain). In this respect, the classical theories focus on ideal networks and, in fact, are also referred to as “kinetic” theories because of their resemblance to ideal gas theories. Chain flexibility and mobility are the essential features of these models, according to which the network chains can experience all possible conformations or spatial arrangements subject to the network’s connectivity. One of the predictions of the classical theories is that the elastic modulus of the network is independent of strain. This results from the assumption that only the entropy at the chain level contributes to the Helmholtz free energy. Experimental evidence, on the other hand, indicates that the modulus decreases significantly with increasing tension or compression, implicating interchain interactions, such as entanglements of some type or other. This has led to the more modern theories of rubber elasticity, such as the constrained-junction or the slip-link theories, which go beyond the single-chain length scale and introduce additional entropy to the Helmholtz free energy at the subchain level.
Streszczenia konferencji na temat "GAUSSIAN BASED HIGHWAY"
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Zhang, Mingheng, Zhengxian Guo, Zhaoyang Liu i Xing Wan. "Research of Driving Fatigue Detection Based on Gaussian Mixture Hidden Markov Model". W 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.
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Liu, Wenjun, Yulin Zhai, Guang Chen i Alois Knoll. "Gaussian Process based Model Predictive Control for Overtaking Scenarios at Highway Curves". W 2022 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2022. http://dx.doi.org/10.1109/iv51971.2022.9827233.
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Shinn, Tyler, Richard Carpenter i Roger C. Fales. "State Estimation Techniques for Axial Piston Pump Health Monitoring". W ASME/BATH 2015 Symposium on Fluid Power and Motion Control. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/fpmc2015-9621.
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Catastrophic failures of hydraulic pumps can lead to significant machine downtime. In the mining and quarry sector this can lead to losses in the tens of thousands of dollars per hour. Predicting pump failures before they occur could lead to substantial savings for equipment owners. This work focuses on developing a pump health strategy using physics-based models of a load sense steering system typically found on off-highway machines. State observers are developed that estimate pump swashplate position in order to determine a theoretical pump flow. Pump efficiency is predicted using actual flow estimates based on measured cylinder velocities and compared to the estimated theoretical pump flow. The typical Kalman filter (KF) is implemented and compared to that of a Sequential Monte Carlo method, the Particle Filter. Observability is examined to determine the feasibility of the KF. The Particle Filter algorithm is considered for its ability to deal nicely with non-linear models with non-Gaussian noise terms. Results show that the system is observable using a limited number of measurements, for example, only pressure measurements. The two methods of estimating states give comparable results when applied to the simulated model. A leakage fault is introduced to the system. An extended Kalman filter (EKF) is used to estimate volumetric efficiency with the unknown change in leakage coefficient using state and parameter estimation. The KF was found to be unable to accurately estimate the changes in volumetric efficiency with the leakage.
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Mamann, Hadriel, Thomas Nieddu, Mathieu Bozzio, Félix Hoffet, Félix Garreau de Loubresse, Eleni Diamanti, Alban Urvoy i Julien Laurat. "Quantum cryptographic protocol implementation using a highly-efficient cold-atom-based quantum memory". W CLEO: Fundamental Science. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/cleo_fs.2023.ff2a.3.
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We report on a highly-efficient cold-atom-based quantum memory and its use to perform a quantum money protocol. We also simulate the multi-mode capacity of this quantum memory using Hermite-Gaussian modes of light.
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Li, Meng, Mohammad Kazem Sadoughi, Zhen Hu i Chao Hu. "System Reliability Analysis Using Hybrid Gaussian Process Model". W 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.
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Abstract This paper proposes a system reliability analysis method based on the hybrid of multivariate Gaussian process (MGP) and univariate Gaussian process (UGP) models, named as hybrid Gaussian process-based system reliability analysis (HGP-SRA). MGP and UGP models are selectively constructed for the components of a complex engineered system: MGP models are constructed over the groups of highly interdependent components and the individual UGP models are built over the components which are relatively independent of one another. A nonlinear-dependence measure, namely the randomized dependence coefficient, is adopted to adaptively learn and quantify the pairwise dependencies of the components with both linear and nonlinear dependency patterns. In the proposed HGP-SRA method, initial hybrid Gaussian process (HGP) models are first constructed with a set of near-random samples and these surrogate models are then updated with new samples that are sequentially identified based on the acquisition function named as multivariate probability of improvement (MPI). The results of two mathematical and a real-world engineering case studies suggest that the proposed method can achieve better accuracy and efficiency in system reliability estimation than the benchmark surrogate-based methods.
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Leahu, Haralambie, Michael Kaisers i Tim Baarslag. "Automated Negotiation with Gaussian Process-based Utility Models". W 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.
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Designing agents that can efficiently learn and integrate user's preferences into decision making processes is a key challenge in automated negotiation. While accurate knowledge of user preferences is highly desirable, eliciting the necessary information might be rather costly, since frequent user interactions may cause inconvenience. Therefore, efficient elicitation strategies (minimizing elicitation costs) for inferring relevant information are critical. We introduce a stochastic, inverse-ranking utility model compatible with the Gaussian Process preference learning framework and integrate it into a (belief) Markov Decision Process paradigm which formalizes automated negotiation processes with incomplete information. Our utility model, which naturally maps ordinal preferences (inferred from the user) into (random) utility values (with the randomness reflecting the underlying uncertainty), provides the basic quantitative modeling ingredient for automated (agent-based) negotiation.
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Zhao, Wentian, Shaojie Wang, Zhihuai Xie, Jing Shi i Chenliang Xu. "GAN-EM: GAN Based EM Learning Framework". W 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.
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Expectation maximization (EM) algorithm is to find maximum likelihood solution for models having latent variables. A typical example is Gaussian Mixture Model (GMM) which requires Gaussian assumption, however, natural images are highly non-Gaussian so that GMM cannot be applied to perform image clustering task on pixel space. To overcome such limitation, we propose a GAN based EM learning framework that can maximize the likelihood of images and estimate the latent variables. We call this model GAN-EM, which is a framework for image clustering, semi-supervised classification and dimensionality reduction. In M-step, we design a novel loss function for discriminator of GAN to perform maximum likelihood estimation (MLE) on data with soft class label assignments. Specifically, a conditional generator captures data distribution for K classes, and a discriminator tells whether a sample is real or fake for each class. Since our model is unsupervised, the class label of real data is regarded as latent variable, which is estimated by an additional network (E-net) in E-step. The proposed GAN-EM achieves state-of-the-art clustering and semi-supervised classification results on MNIST, SVHN and CelebA, as well as comparable quality of generated images to other recently developed generative models.
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Guo, Tuan, Jacques Albert, Chengkun Chen, Alexei Ivanov i Albane Laronche. "Highly accurate micro-displacement measurement based on Gaussian-chirped tilted fiber Bragg grating". W 19th International Conference on Optical Fibre Sensors, redaktor David D. Sampson. SPIE, 2008. http://dx.doi.org/10.1117/12.785637.
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Li, Guang, i J. Quincy Brown. "Optimizing Imaging Throughput and Resolution in Light Sheet Microscopy Using Deep-learning-based Beam Shape Translation". W Novel Techniques in Microscopy. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/ntm.2023.ntu1c.3.
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A conditional generative adversarial network (cGAN) approach to optimize resolution or contrast and field-of-view (FOV) in beam-scanned light sheet microscopy by beam shape translation is demonstrated. Large FOV images acquired with collimated (pencil) beam (weakly-focused) illumination are used to predict large FOV, but higher contrast, images mimicking Gaussian-beam (highly focused) illumination. In this way, imaging throughput and resolution is improved.
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Lin, Po Ting, Wei-Hao Lu i Shu-Ping Lin. "A Comprehensive Investigation of Ensembles of Gaussian-Based and Gradient-Based Transformed Reliability Analyses: When and How to Use Them". W 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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In the past few years, researchers have begun to investigate the existence of arbitrary uncertainties in the design optimization problems. Most traditional reliability-based design optimization (RBDO) methods transform the design space to the standard normal space for reliability analysis but may not work well when the random variables are arbitrarily distributed. It is because that the transformation to the standard normal space cannot be determined or the distribution type is unknown. The methods of Ensemble of Gaussian-based Reliability Analyses (EoGRA) and Ensemble of Gradient-based Transformed Reliability Analyses (EGTRA) have been developed to estimate the joint probability density function using the ensemble of kernel functions. EoGRA performs a series of Gaussian-based kernel reliability analyses and merged them together to compute the reliability of the design point. EGTRA transforms the design space to the single-variate design space toward the constraint gradient, where the kernel reliability analyses become much less costly. In this paper, a series of comprehensive investigations were performed to study the similarities and differences between EoGRA and EGTRA. The results showed that EGTRA performs accurate and effective reliability analyses for both linear and nonlinear problems. When the constraints are highly nonlinear, EGTRA may have little problem but still can be effective in terms of starting from deterministic optimal points. On the other hands, the sensitivity analyses of EoGRA may be ineffective when the random distribution is completely inside the feasible space or infeasible space. However, EoGRA can find acceptable design points when starting from deterministic optimal points. Moreover, EoGRA is capable of delivering estimated failure probability of each constraint during the optimization processes, which may be convenient for some applications.