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检索条件"任意字段=19th International Conference on Logic Programming"

共 2631 条记录，以下是2491-2500 订阅

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Semantic Evidential Lane Grids with Prior Maps for Autonomous Navigation

Semantic Evidential Lane Grids with Prior Maps for Autonomou...

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IEEE international conference on Intelligent Transportation Systems

作者： Chunlei Yu Veronique Cherfaoui Philippe Bonnifait Sorbonne Universites Universite de Technologie de Compiegne

ISBN: (纸本)9781509018901

Real-time modeling of the surrounding environment is a key functionality for autonomous navigation. Bird view grid-based approaches have interesting advantages compared to feature-based ones. Methods able to encode occupancy information and to manage perception uncertainty in dynamic environments are quite well known but very few studies have been carried out on encoding semantic information in grids. this kind of information can be crucial in many situations in order to make the vehicle able to follow basic road rules, such as lane keeping or lane changes. Usual approaches often detect lane markings using on-board cameras or lidars but the problem is tricky when the road is multi-lane or in challenging weather conditions. In this work, we propose to tackle this problem by using a vectorial prior map that stores detailed lane level information. From a given pose estimate provided by a localization system, we propose an evidential model that encodes lane information into grids by propagating the pose uncertainty on every cell. this evidential model is compared with a classical Bayesian one and some of its special characteristics are highlighted. Real results carried on public roads with the real-time software are reported to support the comparison.

关键词： Uncertainty Roads Vehicles Semantics Navigation Image segmentation Probabilistic logic

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Community Detection Approach Via Graph Regularized Non-Negative Matrix Factorization

Community Detection Approach Via Graph Regularized Non-Negat...

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international conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Amin Ul Haq Jian Ping Li Ghufran Ahmad Khan Jalaluddin Khan Mohammad Ishrat Abhishek Guru Bless Lord Y. Agbley University of Electronic Science and Technology of China Chengdu China Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Southwest Jiaotong University Chengdu China

ISBN: (纸本)9781665493901

One of the most important problems towards studying complicated networks is community detection. the methodology of non-negative matrix factorization (NMF) has lately emerged as among the hottest research issues within community detection because of its ability to reveal natural structures and trends in high-dimensional information. the primary difficulty is that most community detection methods are hampered by the issue of nodes belonging to several communities. We will use the NMF technique in this work to tackle this issue by creating a novel mathematical function. In addition, we will include a regularized factor for simulating latent embedding space and a correlation factor to prevent overlap inside nodes that belong to various communities. Following that, the entire objective function will employ an optimization approach to arrive at the variable values that are ideal. Finally, we assess the effectiveness of different methodologies on real networks. According to experimental findings, the introduced approach is better among the other state-of-the-art methodology.

关键词： Correlation Information processing Media Benchmark testing Market research Linear programming Optimization

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Robust Environment Perception for the Audi Autonomous Driving Cup

Robust Environment Perception for the Audi Autonomous Drivin...

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IEEE international conference on Intelligent Transportation Systems

作者： Florian Kuhnt Micha Pfeiffer Peter Zimmer David Zimmerer Jan-Markus Gomer Vitali Kaiser Ralf Kohlhaas J. Marius Zollner FZI Research Center for Information Technology Haid-und-Neu-Str. 10-14 76131 Karlsruhe Germany

ISBN: (纸本)9781509018901

One of the biggest challenges towards fully automated driving is achieving robustness. Autonomous vehicles will have to fully recognize their environment even in harsh weather conditions. Additionally, they have to be able to detect sensor and algorithm failures and react properly to keep the vehicle in a safe state. these two challenges are addressed exemplarily on miniature cars. We extend the approach of Compositional Hierarchical Models [1] by temporal fusion to achieve a robust environment perception. the increased association problem is overcome by a grid-based approximation and a voting system. System performance assessment surveils the system's performance and reacts with driving function degradation or activation of specialized algorithms. the approach was evaluated at the final of the Audi Autonomous Driving Cup 2016. A video shows the advanced driving capabilities under harsh environment conditions and the source code is available for download.

关键词： Roads Automobiles Robustness Layout Probabilistic logic System performance

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Improving the Performance of Arrival on Time in Stochastic Shortest Path Problem

Improving the Performance of Arrival on Time in Stochastic S...

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IEEE international conference on Intelligent Transportation Systems

作者： Yaoxin Wu Wei Chen Xuexi Zhang Guangjun Liao School of Automation Guangdong University of Technology Guangzhou China School of Forensic Science and Technology Guangdong Police College Guangzhou China

ISBN: (纸本)9781509018901

Facing various uncertainties in real world traffic, navigation services are typically formulated as a certain stochastic shortest path problem (SSPP). In the past several years, many stochastic objectives in SSPP has been proposed, among which maximizing the probability of arrival on time draws certain attentions from researchers as it takes, besides absolute travel time, an extra dimension of information (user-specified deadlines) into consideration. this paper extends a recently proposed data-driven approach for SSPP in the following two aspects: (1) when deadline is loose enough, the original method may return more than one paths since all of them meet deadline with 100% probability. We propose to return a unique path with the least number of intersections by reformulating the objective function. (2) we extend the arrive-on-time problem to the case of visiting several fixed locations, which is a common scenario in real world application. Experimental results show the accuracy and effectiveness of the improved data-driven method.

关键词： Linear programming Shortest path problem Delays Minimization Vehicles Stochastic processes Roads

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BISOS: Backwards Incremental System Optimum Search Algorithm for Fast Socially Optimal Traffic Assignment

BISOS: Backwards Incremental System Optimum Search Algorithm...

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IEEE international conference on Intelligent Transportation Systems

作者： Jordan Ivanchev Daniel Zehe Vaisagh Viswanathan Suraj Nair Alois Knoll TUM CREATE 1 CREATE Way 10-02 CREATE Tower Singapore 138602

ISBN: (纸本)9781509018901

this paper presents an algorithm, called the Backwards Incremental System Optimum Search (BISOS) for achieving system near-optimum traffic assignment by incrementally limiting accessibility of roads for a chosen set of agents. the described algorithm redistributes traffic volumes homogeneously around the city and converges significantly faster than existing methods for system optimum computation in current literature. Furthermore, as previous methods have mainly been developed for theoretical purposes, the solutions provided by them do not contain all the necessary information for a practical implementation such as explicit paths for the commuting population. In contrast, the BISOS algorithm preserves the information about the exact paths of all commuters, throughout the whole process of computing the system optimum assignment. Furthermore, a realistic traffic scenario is simulated using Singapore as a case study by utilizing survey and GPS traffic data. the BISOS routing method needs 15 times less routing computations to get within 1% of the optimal solution for a simulated scenario compared to conventional methods for system optimum computation.

关键词： Vehicles Roads Sociology Statistics Routing Linear programming Urban areas

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INTEGRATIVE LABELING BASED STATISTICAL COLOR MODELS WIth APPLICATION TO SKIN DETECTION

INTEGRATIVE LABELING BASED STATISTICAL COLOR MODELS WITH APP...

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IEEE international conference on Image Processing

作者： Mingzhi Dong Liang Yin Jun Guo Weihong Deng Weiran Xu Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China

To alleviate the workload of labeling before estimating certain color distributions, integrative labeling is introduced, which merely needs to figure out whether a picture contains positive-class regions or not and then all pixels of the picture are treated as positive or negative class training samples. Integrative labeling, however, results in heavy mixture of training samples. thus traditional generative density estimation methods can't be used directly in that they perform poorly with heavily polluted training samples. In this paper, by utilizing the prior knowledge of high separability between positive and negative class color distributions, a discriminative learning based GMM(DiscGMM) is proposed for integrative labeling. Besides generating the polluted positive-class samples with comparatively high probability, optimal parameters found by DiscGMM also enjoy a comparatively low probability of generating negative-class samples. the parameter learning problem is solved by a modified Expectation Maximization (EM) algorithm. In an integrative labeling experiment of skin detection, DiscGMM is testified to enjoy much better performance than generative density estimation methods and shows qualified results.

关键词： Labeling Training Skin Image color analysis Histograms Estimation Linear programming

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Consistency Checking in an Infrastructure for Large-Scale Generative programming 04

Consistency Checking in an Infrastructure for Large-Scale Ge...

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Proceedings of the 19th IEEE international conference on Automated software engineering

作者： Axel Rauschmayer Alexander Knapp Martin Wirsing Universität München

ISBN: (纸本)9780769521312

Ubiquitous computing increases the pressure on the software industry to produce ever more and error-free code. Two recipes from automated programming are available to meet this challenge: On the one hand, generative programming raises the level of abstraction in software development by describing problems in high-level domain-specific languages and making them executable. On the other hand, in situations where one needs to produce a family of similar programs, product line engineering supports code reuse by composing programs from a set of common assets (or features). AHEAD (Algebraic Hierarchical Equations for Application Design) is a framework for generative programming and product line engineering that achieves additional productivity gains by scaling feature composition up. Our contribution is GRAFT, a calculus that gives a formal foundation to AHEAD and provides several mechanisms for making sure that feature combinations are legal and that features in themselves are consistent.

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19th international conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2022

作者： Miralles-Dolz, Enrique Gray, Ander Patelli, Edoardo Ferson, Scott Institute for Risk and Uncertainty University of Liverpool Liverpool United Kingdom United Kingdom Atomic Energy Authority Abingdon United Kingdom Centre for Intelligent Infrastructure University of Strathclyde Glasgow United Kingdom

ISBN: (纸本)9783031089701

We present a correlated and gate which may be used to propagate uncertainty and dependence through Boolean functions, since any Boolean function may be expressed as a combination of and and not operations. We argue that the and gate is a bivariate copula family, which has the interpretation of constructing bivariate Bernoulli random variables following a given Pearson correlation coefficient and marginal probabilities. We show how this copula family may be used to propagate uncertainty in the form of probabilities of events, probability intervals, and probability boxes, with only partial or no knowledge of the dependency between events, expressed as an interval for the correlation coefficient. these results generalise previous results by Fréchet on the conjunction of two events with unknown dependencies. We show an application propagating uncertainty through a fault tree for a pressure tank. this paper comes with an open-source Julia library for performing uncertainty logic. © 2022, Springer Nature Switzerland AG.

关键词： Boolean functions

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A Stochastic programming Model for Evaluating Real Options in Wind Power Investment Projects

A Stochastic Programming Model for Evaluating Real Options i...

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2012 IEEE 19th international conference on Industrial Engineering and Engineering Management(IE&EM 2012)

作者： Han Qin L.K.Chu Department of Industrial and Manufacturing Systems and Engineering The University of Hong Kong

this study is concerned with the evaluation of wind power projects under the Clean Development Mechanism (CDM), not only for the purpose of CDM verification, but also for the financing of the project. A real options model is developed in this paper to evaluate the investment decisions on wind power project. the model obtains the real value of the project and determines the optimal time to invest wind power project. Stochastic programming is employed to evaluate the real options model, and a scenario tree, generated by path-based sampling method and LHS discretisation, is constructed to approximate the original stochastic program.

关键词： Clean Development Mechanism (CDM), stochastic programming, real options, wind power investment

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A Novel Fuzzy Self Tuning Technique of Single Neuron PID Controller for Brushless DC Motor 19

A Novel Fuzzy Self Tuning Technique of Single Neuron PID Con...

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19th international Middle East Power Systems conference (MEPCON)

作者： Ghany, M. A. Abdel Shamseldin, Mohamed. A. Ghany, A. M. Abdel Nanda Univ Elect Power & Machines Dept Bani Sweif Egypt Future Univ Egypt Mechatron Dept New Cairo Egypt Helwan Univ Elect Power & Machines Dept Cairo Egypt

ISBN: (纸本)9781538609903

In this paper, a combination ANN/Fuzzy techniques are used to design a Novel Fuzzy Single Neuron PID (NFSNPID) controller to achieve high performance brushless DC motor. the design steps include two parts. the first part uses the genetic algorithm (GA) to obtain the optimum parameters of Single Neuron PID (SNPID) controller, while the former deals with the design of fuzzy logic control to update the weights of SNPID control online. To demonstrate the designed controller effectiveness, a comparative study is made with between the NFSNPID, Conventional Fuzzy Single Neuron PID CFSNPID and SNPID. All controllers were used to drive, separately, the brushless DC motor against the sudden change of load and operating speed. the performed simulations show better results that motivate for further investigations.

关键词： Brushless DC (BLDC) Motor Fuzzy logic Control Single Neuron (SN) PID Control Conventional Fuzzy Single Neuron PID CFSNPID Novel Fuzzy Single Neuron PID (NFSNPID) Genetic Algorithm (GA)

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