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Готові списки джерел за темами / Maximum consensus

Добірка наукової літератури з теми "Maximum consensus"

Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 29 січня 2023

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Статті в журналах з теми "Maximum consensus"

1

Wang, Xin, Jianping He, Peng Cheng, and Jiming Chen. "Differentially Private Maximum Consensus." IFAC-PapersOnLine 50, no. 1 (July 2017): 9509–14. http://dx.doi.org/10.1016/j.ifacol.2017.08.1597.

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2

Aßfalg, André, and Edgar Erdfelder. "CAML—Maximum likelihood consensus analysis." Behavior Research Methods 44, no. 1 (July 27, 2011): 189–201. http://dx.doi.org/10.3758/s13428-011-0138-0.

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3

Venkategowda, Naveen K. D., and Stefan Werner. "Privacy-Preserving Distributed Maximum Consensus." IEEE Signal Processing Letters 27 (2020): 1839–43. http://dx.doi.org/10.1109/lsp.2020.3029706.

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4

Wiedermann, Gerhard. "Maximum Immunization for Travel: Consensus." Journal of Travel Medicine 2, no. 3 (September 1, 1995): 191–92. http://dx.doi.org/10.1111/j.1708-8305.1995.tb00652.x.

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5

Axmann, J., and C. Brenner. "MAXIMUM CONSENSUS LOCALIZATION USING LIDAR SENSORS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2021 (June 17, 2021): 9–16. http://dx.doi.org/10.5194/isprs-annals-v-2-2021-9-2021.

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Abstract. Real world localization tasks based on LiDAR usually face a high proportion of outliers arising from erroneous measurements and changing environments. However, applications such as autonomous driving require a high integrity in all of their components, including localization. Standard localization approaches are often based on (recursive) least squares estimation, for example, using Kalman filters. Since least squares minimization shows a strong susceptibility to outliers, it is not robust.In this paper, we focus on high integrity vehicle localization and investigate a maximum consensus localization strategy. For our work, we use 2975 epochs from a Velodyne VLP-16 scanner (representing the vehicle scan data), and map data obtained using a Riegl VMX-250 mobile mapping system. We investigate the effects of varying scene geometry on the maximum consensus result by exhaustively computing the consensus values for the entire search space. We analyze the deviations in position and heading for a circular course in a downtown area by comparing the estimation results to a reference trajectory, and show the robustness of the maximum consensus localization.

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6

Nakamura, Masahiro, Hideaki Ishii, and Seyed Mehran Dibaji. "Maximum-Based Consensus and Its Resiliency." IFAC-PapersOnLine 51, no. 23 (2018): 283–88. http://dx.doi.org/10.1016/j.ifacol.2018.12.049.

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7

Wen, Fei, Rendong Ying, Zheng Gong, and Peilin Liu. "Efficient Algorithms for Maximum Consensus Robust Fitting." IEEE Transactions on Robotics 36, no. 1 (February 2020): 92–106. http://dx.doi.org/10.1109/tro.2019.2943061.

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8

Chin, Tat-Jun, and David Suter. "The Maximum Consensus Problem: Recent Algorithmic Advances." Synthesis Lectures on Computer Vision 7, no. 2 (February 27, 2017): 1–194. http://dx.doi.org/10.2200/s00757ed1v01y201702cov011.

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9

Nakamura, Masahiro, Hideaki Ishii, and Seyed Mehran Dibaji. "Resiliency against malicious agents in maximum-based consensus." SICE Journal of Control, Measurement, and System Integration 14, no. 1 (January 1, 2021): 279–90. http://dx.doi.org/10.1080/18824889.2021.1988396.

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10

EDDY, SEAN R., GRAEME MITCHISON, and RICHARD DURBIN. "Maximum Discrimination Hidden Markov Models of Sequence Consensus." Journal of Computational Biology 2, no. 1 (January 1995): 9–23. http://dx.doi.org/10.1089/cmb.1995.2.9.

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Більше джерел

Дисертації з теми "Maximum consensus"

1

Sampath, Srinath. "Analysis of Agreement Between Two Long Ranked Lists." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1385415346.

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2

Truong, Ha-Giang. "Robust fitting: Assisted by semantic analysis and reinforcement learning." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2567.

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Анотація:

Many computer vision applications require robust model estimation from a set of observed data. However, these data usually contain outliers, due to imperfect data acquisition or pre-processing steps, which can reduce the performance of conventional model-fitting methods. Robust fitting is thus critical to make the model estimation robust against outliers and reach stable performance. All of the contributions made in this thesis are for maximum consensus. In robust model fitting, maximum consensus is one of the most popular criteria, which aims to estimate the model that is consistent to as many observations as possible, i.e. obtain the highest consensus. The thesis makes contributions in two aspects of maximum consensus, one is non-learning based approaches and the other is learning based approaches. The first motivation for our work is the remarkable progress in semantic segmentation in recent years. Semantic segmentation is a useful process and is usually available for scene understanding, medical image analysis, and virtual reality. We propose novel methods, which make use of semantic segmentation, to improve the efficiency of two robust non-learning based algorithms. Another motivation for our contributions is the advances in reinforcement learning. In the thesis, a novel unsupervised learning framework is proposed to learn (without labelled data) to solve robust estimation directly. In particular, we formulate robust fitting problem as a special case of goal-oriented learning, and adopt the Reinforcement Learning framework as the basis of our approach. Our approach is agnostic to the input features and can be generalized to various practical applications.

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3

Whipps, Gene Thomas. "Contributions to Distributed Detection and Estimation over Sensor Networks." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1502970194073045.

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4

Izza, Yacine. "Informatique ubiquitaire : techniques de curage d'informations perverties On the extraction of one maximal information subset that does not conflit with multiple contexts Extraction d'un sous-ensemble maximal qui soit cohérent avec des contextes mutuellement contradictoires On computing one max-inclusion consensus On admissible consensuses Boosting MCSes enumeration." Thesis, Artois, 2018. http://www.theses.fr/2018ARTO0405.

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Cette thèse étudie une approche possible de l'intelligence artificielle pour la détection et le curage d'informations perverties dans les bases de connaissances des objets et composants intelligents en informatique ubiquitaire. Cette approche est traitée d'un point de vue pratique dans le cadre du formalisme SAT; il s'agit donc de mettre en œuvre des techniques de filtrage d'incohérences dans des bases contradictoires. Plusieurs contributions sont apportées dans cette thèse. Premièrement, nous avons travaillé sur l'extraction d'un ensemble maximal d'informations qui soit cohérent avec une série de contextes hypothétiques. Nous avons proposé une approche incrémentale pour le calcul d'un tel ensemble (AC-MSS). Deuxièmement, nous nous sommes intéressés à la tâche d'énumération des ensembles maximaux satisfaisables (MSS) ou leurs complémentaires les ensembles minimaux rectificatifs (MCS) d'une instance CNF insatisfaisable. Dans cette contribution, nous avons introduit une technique qui améliore les performances des meilleures approches pour l'énumération des MSS/MCS. Cette méthode implémente le paradigme de rotation de modèle qui permet de calculer des ensembles de MCS de manière heuristique et efficace. Finalement, nous avons étudié une notion de consensus permettant réconcilier des sources d'informations. Cette forme de consensus peut être caractérisée par différents critères de préférence, comme le critère de maximalité. Une approche incrémentale de calcul d'un consensus maximal par rapport à l'inclusion ensembliste a été proposée. Nous avons également introduit et étudié la concept de consensus admissible qui raffine la définition initialement proposée du concept de consensus
This thesis studies a possible approach of artificial intelligence for detecting and filtering inconsistent information in knowledge bases of intelligent objects and components in ubiquitous computing. This approach is addressed from a practical point of view in the SAT framework;it is about implementing a techniques of filtering inconsistencies in contradictory bases. Several contributions are made in this thesis. Firstly, we have worked on the extraction of one maximal information set that must be satisfiable with multiple assumptive contexts. We have proposed an incremental approach for computing such a set (AC-MSS). Secondly, we were interested about the enumeration of maximal satisfiable sets (MSS) or their complementary minimal correction sets (MCS) of an unsatisfiable CNF instance. In this contribution, a technique is introduced that boosts the currently most efficient practical approaches to enumerate MCS. It implements a model rotation paradigm that allows the set of MCS to be computed in an heuristically efficient way. Finally, we have studied a notion of consensus to reconcile several sources of information. This form of consensus can obey various preference criteria, including maximality one. We have then developed an incremental algorithm for computing one maximal consensus with respect to set-theoretical inclusion. We have also introduced and studied the concept of admissible consensus that refines the initial concept of consensus

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5

Cheng, Li-chen, and 鄭麗珍. "Mining maximum consensus sequences from group ranking data." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/58303646266345835813.

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博士
國立中央大學
資訊管理研究所
96
In the last decade, the problem of getting a consensus group ranking from users’ ranking data has received increased attention due to its widespread applications. Previous research solved this problem by consolidating the opinions of all users, thereby obtaining an ordering list of all items that represent the achieved consensus. The weakness of this approach, however, is that it always produces a ranking list of all items, regardless of how many conflicts exist among users. This work rejects the forced agreement of all items. Instead, we define a new concept, maximum consensus sequences, which are the longest ranking lists of items that agree with the majority and disagree only with the minority. Based on this concept, we use two kinds of input data, individual’s total ranking and individual’s partial rankings, to develop algorithms to discover maximum consensus sequences and also to identify conflict items that need further negotiation. Besides, we propose another algorithm to achieve personalized rankling list which can be used in recommender system. Extensive experiments are carried out using synthetic data sets, and the results indicate that the proposed methods are computationally efficient.

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6

Le, Huu Minh. "New algorithmic developments in maximum consensus robust fitting." Thesis, 2018. http://hdl.handle.net/2440/115183.

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In many computer vision applications, the task of robustly estimating the set of parameters of a geometric model is a fundamental problem. Despite the longstanding research efforts on robust model fitting, there remains significant scope for investigation. For a large number of geometric estimation tasks in computer vision, maximum consensus is the most popular robust fitting criterion. This thesis makes several contributions in the algorithms for consensus maximization. Randomized hypothesize-and-verify algorithms are arguably the most widely used class of techniques for robust estimation thanks to their simplicity. Though efficient, these randomized heuristic methods do not guarantee finding good maximum consensus estimates. To improve the randomize algorithms, guided sampling approaches have been developed. These methods take advantage of additional domain information, such as descriptor matching scores, to guide the sampling process. Subsets of the data that are more likely to result in good estimates are prioritized for consideration. However, these guided sampling approaches are ineffective when good domain information is not available. This thesis tackles this shortcoming by proposing a new guided sampling algorithm, which is based on the class of LP-type problems and Monte Carlo Tree Search (MCTS). The proposed algorithm relies on a fundamental geometric arrangement of the data to guide the sampling process. Specifically, we take advantage of the underlying tree structure of the maximum consensus problem and apply MCTS to efficiently search the tree. Empirical results show that the new guided sampling strategy outperforms traditional randomized methods. Consensus maximization also plays a key role in robust point set registration. A special case is the registration of deformable shapes. If the surfaces have the same intrinsic shapes, their deformations can be described accurately by a conformal model. The uniformization theorem allows the shapes to be conformally mapped onto a canonical domain, wherein the shapes can be aligned using a M¨obius transformation. The problem of correspondence-free M¨obius alignment of two sets of noisy and partially overlapping point sets can be tackled as a maximum consensus problem. Solving for the M¨obius transformation can be approached by randomized voting-type methods which offers no guarantee of optimality. Local methods such as Iterative Closest Point can be applied, but with the assumption that a good initialization is given or these techniques may converge to a bad local minima. When a globally optimal solution is required, the literature has so far considered only brute-force search. This thesis contributes a new branch-and-bound algorithm that solves for the globally optimal M¨obius transformation much more efficiently. So far, the consensus maximization problems are approached mainly by randomized algorithms, which are efficient but offer no analytical convergence guarantee. On the other hand, there exist exact algorithms that can solve the problem up to global optimality. The global methods, however, are intractable in general due to the NP-hardness of the consensus maximization. To fill the gap between the two extremes, this thesis contributes two novel deterministic algorithms to approximately optimize the maximum consensus criterion. The first method is based on non-smooth penalization supported by a Frank-Wolfe-style optimization scheme, and another algorithm is based on Alternating Direction Method of Multipliers (ADMM). Both of the proposed methods are capable of handling the non-linear geometric residuals commonly used in computer vision. As will be demonstrated, our proposed methods consistently outperform other heuristics and approximate methods.
Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Computer Science, 2018

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7

Doung, Ming-Chang, and 董佲昌. "An Exact Algorithm for Constructing a Maximum Consensus tree from Rooted Triples." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/98998582712411857862.

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Анотація:

碩士
國立中正大學
資訊工程研究所
92
In this thesis, we study the important problem that combines the evolutionary trees on overlapping leaf sets into one tree in computational biology. But the input trees are hard to consistent. So there is an optimization problem about consensus tree. Then we give a branch-and-bound algorithm to find a maximum consensus tree from rooted triple. We implement the algorithm and test it to prove our algorithm better than the other algorithms in some situation.

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8

Tsai, Pei-Chun, and 蔡佩君. "An Improved Exact Algorithm for Constructing a Maximum Consensus Tree from Rooted Triples." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/49372232669428053043.

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Анотація:

碩士
國立中正大學
資訊工程所
93
The maximum consensus tree problem is to find a rooted binary tree that can satisfy the input triples as many as possible. In this thesis, we use a branch-and-bound algorithm with the persistent data structure to solve the maximum consensus tree problem. Using the persistent data structure, we can enhance the running time for branching and use less memory space to save each subproblem. Due to these two properties, the total running time is better, and we can deal with instances with more species.

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9

Hsu, Chih-Cheng, and 許志成. "Genetic Algorithms for Constructing multiple Consensus Evolutionary Trees Using Bootstrapping and Maximum Likelihood Criterion." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/d9kmj8.

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Анотація:

碩士
銘傳大學
資訊工程學系碩士班
92
Phylogeny analysis is a process which derives the branching and mutations happened during the evolution. There are two main phylogeny analysis methods: distance-based and character-based; and there are two types of evolutionary trees: rooted and unrooted. This paper presents a maximum criterion-based phylogeny analysis method for unrooted trees. To increase the confidence level of the derived evolutionary trees, we use bootstrapping and consensus analysis in conjunction with a genetic algorithm for crossover and mutation between evolutionary trees. Multiple datasets of DNA sequences of species are generated using bootstrapping, the evolutionary trees are evaluated using these datasets and evolve to optimal trees by a genetic algorithm. The evolutionary trees are clustered based on similarity, a consensus tree is produced for each cluster. The experimental results manifest that the consensus trees produced by our system are highly similar to those validated by biologists.

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Книги з теми "Maximum consensus"

1

Chin, Tat-Jun, and David Suter. The Maximum Consensus Problem. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-031-01818-3.

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2

Suter, David, Gerard Medioni, Sven Dickinson, and Tat-Jun Chin. Maximum Consensus Problem: Recent Algorithmic Advances. Morgan & Claypool Publishers, 2017.

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3

Suter, David, Gerard Medioni, Sven Dickinson, and Tat-Jun Chin. Maximum Consensus Problem: Recent Algorithmic Advances. Morgan & Claypool Publishers, 2017.

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4

Suter, David, and Tat-Jun Chin. Maximum Consensus Problem: Recent Algorithmic Advances. Springer International Publishing AG, 2017.

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5

Schake, Kori. The National Security Process. Edited by Derek S. Reveron, Nikolas K. Gvosdev, and John A. Cloud. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190680015.013.12.

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Removing the friction of competing perspectives and bureaucratic interests by the various agencies of government (along with their supporters in the Congress and beneficiaries among businesses, interest groups, and the public) that would foster “unity of effort” and the ability to prioritize activity, budget accordingly, carry out policies crisply, and convey a consistent message by our government is chimerical. The interagency system cannot be structured to maximum efficiency in the United States. Efficiency is radically beside the point, because the purpose of the structure is to maximize consensus on the activity to be undertaken. The politics cannot be leached out of policymaking—nor should they be—because politics give policymaking its legitimacy.

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6

Berrill, Andrew, and Pawan Gupta. General principles of regional anaesthesia. Edited by Philip M. Hopkins. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199642045.003.0052.

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Regional anaesthesia is now extremely safe in experienced hands. The vast majority of upper and lower limb procedures can now be performed with either a peripheral regional block alone or in combination with a general anaesthetic. Neuraxial blocks can provide reliable postoperative pain relief for operations on the trunk and lower limbs. There is no consensus on the maximum safe dose of local anaesthetics. It is important therefore to use a minimum optimal dose of a local anaesthetic for any nerve block to reduce the risk of toxicity and to improve the success rate. Adjuncts, such as clonidine and dexamethasone, can prolong the duration of the block. Advances in nerve localization methods and block needles have further improved the safety of nerve blocks. There is increasing evidence to show that ultrasound is superior to peripheral nerve stimulation for identifying nerves. Ultrasound also helps in real-time visualization of the spread of the local anaesthetic. Consent, sedation, and support from non-anaesthetic staff play a key role in the success of regional anaesthesia, especially in awake patients. Although serious complications from nerve blocks are uncommon, direct nerve injury is perhaps the most serious complication. Fortunately, these symptoms in the overwhelming majority resolve within a year. This chapter covers the history, factors affecting local anaesthetics, role of adjuncts, nerve localization techniques, and complications of regional anaesthesia. Finally, some suggestions to improve the success and safety of peripheral nerve blocks are discussed.

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7

Dietz, Volker, and Nick S. Ward, eds. Oxford Textbook of Neurorehabilitation. Oxford University Press, 2020. http://dx.doi.org/10.1093/med/9780198824954.001.0001.

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Анотація:

In the new edition of the Oxford Textbook of Neurorehabilitation all chapters have been updated to reflect advances in knowledge in the field of neurorehabilitation. It will be supplemented by additional chapters that reflect novel developments in the field of neurorehabilitation. During recent years there has been a strong evolution in the field of vocational rehabilitation with the aim of helping people after an injury of the nervous system to overcome the barriers and return to employment. A new chapter on self-management strategies deals with building confidence in individuals to manage the medical and emotional aspects of their condition. Furthermore, today the scientific basis for music supported therapy is a much broader to introduce it in this edition. New guidelines and consensus statements became established concerning preclinical research, biomarkers, and outcome measures, in both animal models and human beings. There are new data on attempts (e.g. using stem cells or Nogo antibodies) to restore function after spinal cord injury and stroke. Not all of these therapies and clinical trials have had positive outcomes. One particular area of rapid expansion reflects the use of technology in neurorehabilitation and several chapters remain devoted to this topic in various forms. Still a better understanding of the interactions of technology led therapies and conventional approaches in patients with neurodisability is required. There is still work to be done in defining key components of all neurorehabilitation interventions in order to understand how they might best be delivered for maximum benefit.

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8

Ratel, Sébastien, and Craig A. Williams. Neuromuscular fatigue. Edited by Neil Armstrong and Willem van Mechelen. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198757672.003.0009.

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Scientific evidence supports the proposition that prepubertal children fatigue less than adults when performing whole-body dynamic activities like maximal cycling, running bouts, and maximal voluntary isometric/isokinetic muscle contractions. Although the mechanisms underpinning differences in fatigue between children and adults are not all fully understood, there is a consensus that children experience less peripheral fatigue (i.e. muscular fatigue) than their older counterparts. Central factors may also account for the lower fatigability in children. Some studies report a higher reduction of muscle voluntary activation during fatiguing exercise in prepubertal children compared to adults. This could reflect a strategy of the central nervous system aimed at limiting the recruitment of motor units, in order to prevent any extensive peripheral fatigue. Further studies are required to clarify this proposition.

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Частини книг з теми "Maximum consensus"

1

Chin, Tat-Jun, and David Suter. "The Maximum Consensus Problem." In The Maximum Consensus Problem, 1–19. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-031-01818-3_1.

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2

Chin, Tat-Jun, and David Suter. "Preprocessing for Maximum Consensus." In The Maximum Consensus Problem, 127–49. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-031-01818-3_4.

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3

Chin, Tat-Jun, and David Suter. "Exact Algorithms." In The Maximum Consensus Problem, 81–126. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-031-01818-3_3.

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4

Chin, Tat-Jun, and David Suter. "Approximate Algorithms." In The Maximum Consensus Problem, 21–79. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-031-01818-3_2.

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5

Sung, Wing-Kin. "Greedy Consensus Tree and Maximum Greedy Consensus Tree Problems." In WALCOM: Algorithms and Computation, 305–16. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-10564-8_24.

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6

Zhang, Liang, Houman Rastgar, Demin Wang, and André Vincent. "Maximum Likelihood Estimation Sample Consensus with Validation of Individual Correspondences." In Advances in Visual Computing, 447–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10331-5_42.

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7

Purkait, Pulak, Christopher Zach, and Anders Eriksson. "Maximum Consensus Parameter Estimation by Reweighted $$\ell _1$$ ℓ 1 Methods." In Lecture Notes in Computer Science, 312–27. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78199-0_21.

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8

Kenyeres, Martin, and Jozef Kenyeres. "How to Optimally Reconfigure Average Consensus with Maximum-Degree Weights in Bipartite Regular Graphs." In Software Engineering Application in Systems Design, 189–204. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-21435-6_16.

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9

Theisler, Charles. "Consents and Releases." In Maximum Malpractice Protection, 127–46. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003352464-11.

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10

Torra, Vicenç, Guillermo Navarro-Arribas, and Edgar Galván. "Explaining Recurrent Machine Learning Models: Integral Privacy Revisited." In Privacy in Statistical Databases, 62–73. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57521-2_5.

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Abstract We have recently introduced a privacy model for statistical and machine learning models called integral privacy. A model extracted from a database or, in general, the output of a function satisfies integral privacy when the number of generators of this model is sufficiently large and diverse. In this paper we show how the maximal c-consensus meets problem can be used to study the databases that generate an integrally private solution. We also introduce a definition of integral privacy based on minimal sets in terms of this maximal c-consensus meets problem.

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Тези доповідей конференцій з теми "Maximum consensus"

1

Duan, Xiaoming, Jianping He, Peng Cheng, Yilin Mo, and Jiming Chen. "Privacy Preserving Maximum Consensus." In 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. http://dx.doi.org/10.1109/cdc.2015.7402925.

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2

Zhang, Sai, Cihan Tepedelenlioglu, Mahesh K. Banavar, and Andreas Spanias. "Max-consensus using the soft maximum." In 2013 Asilomar Conference on Signals, Systems and Computers. IEEE, 2013. http://dx.doi.org/10.1109/acssc.2013.6810313.

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3

George, Jemin. "Distributed Maximum Likelihood Using Dynamic Average Consensus." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8461529.

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4

Wang, Zhaowei, Peng Zeng, Mingtuo Zhou, and Dong Li. "Cluster-Based Maximum Consensus Time Synchronization in IWSNs." In 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring). IEEE, 2016. http://dx.doi.org/10.1109/vtcspring.2016.7504162.

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5

Wang, Gang, Kai Liu, Jinxin Wang, and Rui Xue. "Distributed Maximum Correntropy Kalman Filter with Consensus Strategies." In 2021 7th International Conference on Control, Automation and Robotics (ICCAR). IEEE, 2021. http://dx.doi.org/10.1109/iccar52225.2021.9463508.

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6

Le, Huu, Tat-Jun Chin, and David Suter. "RATSAC - Random Tree Sampling for Maximum Consensus Estimation." In 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2017. http://dx.doi.org/10.1109/dicta.2017.8227480.

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7

Le, Huu, Tat-Jun Chin, and David Suter. "An Exact Penalty Method for Locally Convergent Maximum Consensus." In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017. http://dx.doi.org/10.1109/cvpr.2017.48.

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8

Zhang, Erchuan, David Suter, Ruwan Tennakoon, Tat-Jun Chin, Alireza Bab-Hadiashar, Giang Truong, and Syed Zulqarnain Gilani. "Maximum Consensus by Weighted Influences of Monotone Boolean Functions." In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.00876.

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9

He, Jianping, Peng Cheng, Ling Shi, and Jiming Chen. "Time synchronization in WSNs: A maximum value based consensus approach." In 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011). IEEE, 2011. http://dx.doi.org/10.1109/cdc.2011.6161443.

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10

Hasan, Nazmul. "Maximum Allowable Speed on Curve." In 2011 Joint Rail Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/jrc2011-56007.

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Анотація:

It is generally recognized by FRA, AREMA, Amtrak, OSHA, and many other applicable authorities that the maximum acceptable rate of radial acceleration for passengers comfort is 0.1g, where ‘g’ is 9.81 m/s/s. Jerk is limited to 0.03g/s. In the industry the maximum allowable speed (km/h) is calculated by: Vmax=(Ea+Eu′)R11.8 where Eu′ = Blanket unbalance usually greater than design unbalance (mm); Ea = Actual superelevation applied to track (mm); R = Curve radius (m). Clearly the purpose of the equation is to achieve a gain in speed with using the existing spiral at the cost of the comfort limit. It appears that there is a consensus in breaking the standard comfort limit. The value of blanket unbalance varies from operator to operator e.g. 65mm, 75mm, 100mm etc. This variation indicates that there is no consensus in an upper limit beyond standard passenger comfort limit to determine the maximum allowable speed. This is the main reason behind the variation of unbalance, Eu′ adopted by different railways. Other minor reasons are ability of the vehicle to negotiate unbalance, strength of track to withstand lateral load, maintenance standard of track etc. Current use of blanket unbalance superelevation for all types of curves is flawed because it leads to different values of jerk depending on design speed, radius, and incremental unbalance on top of design unbalance. Among these different values of jerk, all values may not be acceptable. The current practice of using a blanket unbalance superelevation may not generate the maximum allowable speed. Intuitively the actual and unbalance superelevation should vary together such that a higher unbalance should go with higher actual superelevation to ensure a consistent comfort. Thus the unbalance should be different for each individual curve. To overcome the weaknesses of using a blanket unbalance superelevation and to gain higher speed, a new formula is suggested.

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