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Artykuły w czasopismach na temat "Constraint weighting"

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Zhang, Jie. "Constraint weighting and constraint domination: a formal comparison". Phonology 24, nr 3 (30.11.2007): 433–59. http://dx.doi.org/10.1017/s0952675707001285.

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The advent of Optimality Theory has revived the interest in articulatorily and perceptually driven markedness in phonological research. To some researchers, the cross-linguistic prevalence of such markedness relations is indication that synchronic phonological grammar should include phonetic details. However, there are at least two distinct ways in which phonetics can be incorporated in an optimality-theoretic grammar: traditional constraint domination and Flemming (2001) 's proposal that the costs of constraint violations should be weighted and summed. I argue that constraint weighting is unnecessary as an innovation in Optimality Theory. The arguments are twofold. First, using constraint families with intrinsic rankings, constraint domination formally predicts the same range of phonological realisations as constraint weighting. Second, with proper constraint definitions and rankings, both the additive effect and the locus effect predicted by constraint weighting can be replicated in constraint domination.
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Gillett, Nathan P. "Weighting climate model projections using observational constraints". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 373, nr 2054 (13.11.2015): 20140425. http://dx.doi.org/10.1098/rsta.2014.0425.

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Projected climate change integrates the net response to multiple climate feedbacks. Whereas existing long-term climate change projections are typically based on unweighted individual climate model simulations, as observed climate change intensifies it is increasingly becoming possible to constrain the net response to feedbacks and hence projected warming directly from observed climate change. One approach scales simulated future warming based on a fit to observations over the historical period, but this approach is only accurate for near-term projections and for scenarios of continuously increasing radiative forcing. For this reason, the recent Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5) included such observationally constrained projections in its assessment of warming to 2035, but used raw model projections of longer term warming to 2100. Here a simple approach to weighting model projections based on an observational constraint is proposed which does not assume a linear relationship between past and future changes. This approach is used to weight model projections of warming in 2081–2100 relative to 1986–2005 under the Representative Concentration Pathway 4.5 forcing scenario, based on an observationally constrained estimate of the Transient Climate Response derived from a detection and attribution analysis. The resulting observationally constrained 5–95% warming range of 0.8–2.5 K is somewhat lower than the unweighted range of 1.1–2.6 K reported in the IPCC AR5.
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Buatoom, Uraiwan, Waree Kongprawechnon i Thanaruk Theeramunkong. "Document Clustering Using K-Means with Term Weighting as Similarity-Based Constraints". Symmetry 12, nr 6 (6.06.2020): 967. http://dx.doi.org/10.3390/sym12060967.

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In similarity-based constrained clustering, there have been various approaches on how to define the similarity between documents to guide the grouping of similar documents together. This paper presents an approach to use term-distribution statistics extracted from a small number of cue instances with their known classes, for term weightings as indirect distance constraint. As for distribution-based term weighting, three types of term-oriented standard deviations are exploited: distribution of a term in a collection (SD), average distribution of a term in a class (ACSD), and average distribution of a term among classes (CSD). These term weightings are explored with the consideration of symmetry concepts by varying the magnitude to positive and negative for promoting and demoting effects of three standard deviations. In k-means, followed the symmetry concept, both seeded and unseeded centroid initializations are investigated and compared to the centroid-based classification. Our experiment is conducted using five English text collections and one Thai text collection, i.e., Amazon, DI, WebKB1, WebKB2, and 20Newsgroup, as well as TR, a collection of Thai reform-related opinions. Compared to the conventional TFIDF, the distribution-based term weighting improves the centroid-based method, seeded k-means, and k-means with the error reduction rate of 22.45%, 31.13%, and 58.96%.
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Jing, Hao, James Pinchin, Chris Hill i Terry Moore. "An Adaptive Weighting based on Modified DOP for Collaborative Indoor Positioning". Journal of Navigation 69, nr 2 (23.09.2015): 225–45. http://dx.doi.org/10.1017/s037346331500065x.

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Indoor localisation has always been a challenging problem due to poor Global Navigation Satellite System (GNSS) availability in such environments. While inertial measurement sensors have become popular solutions for indoor positioning, they suffer large drifts after initialisation. Collaborative positioning enhances positioning robustness by integrating multiple localisation information, especially relative ranging measurements between local users and transmitters. However, not all ranging measurements are useful throughout the whole positioning process and integrating too much data will increase the computation cost. To enable a more reliable positioning system, an adaptive collaborative positioning algorithm is proposed which selects units for the collaborative network and integrates ranging measurement to constrain inertial measurement errors. The algorithm selects the network adaptively from three perspectives: the network geometry, the network size and the accuracy level of the ranging measurements between the units. The collaborative relative constraint is then defined according to the selected network geometry and anticipated measurement quality. In the case of trials with real data, the positioning accuracy is improved by 60% by adjusting the range constraint adaptively according to the selected network situation, while also improving the system robustness.
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Chien, Wei, Chien-Ching Chiu, Po-Hsiang Chen, Yu-Ting Cheng, Eng Hock Lim, Yue-Li Liang i Jia-Rui Wang. "Different Object Functions for SWIPT Optimization by SADDE and APSO". Symmetry 13, nr 8 (24.07.2021): 1340. http://dx.doi.org/10.3390/sym13081340.

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Multiple objective function with beamforming techniques by algorithms have been studied for the Simultaneous Wireless Information and Power Transfer (SWIPT) technology at millimeter wave. Using the feed length to adjust the phase for different objects of SWIPT with Bit Error Rate (BER) and Harvesting Power (HP) are investigated in the broadband communication. Symmetrical antenna array is useful for omni bearing beamforming adjustment with multiple receivers. Self-Adaptive Dynamic Differential Evolution (SADDE) and Asynchronous Particle Swarm Optimization (APSO) are used to optimize the feed length of the antenna array. Two different object functions are proposed in the paper. The first one is the weighting factor multiplying the constraint BER and HP plus HP. The second one is the constraint BER multiplying HP. Simulations show that the first object function is capable of optimizing the total harvesting power under the BER constraint and APSO can quickly converges quicker than SADDE. However, the weighting for the final object function requires a pretest in advance, whereas the second object function does not need to set the weighting case by case and the searching is more efficient than the first one. From the numerical results, the proposed criterion can achieve the SWIPT requirement. Thus, we can use the novel proposed criterion (the second criterion) to optimize the SWIPT problem without testing the weighting case by case.
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Xie, Minghua, Lili Xie i Peidong Zhu. "An Efficient Feature Weighting Method for Support Vector Regression". Mathematical Problems in Engineering 2021 (2.03.2021): 1–7. http://dx.doi.org/10.1155/2021/6675218.

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Support vector regression (SVR) is a powerful kernel-based method which has been successfully applied in regression problems. Regarding the feature-weighted SVR algorithms, its contribution to model output has been taken into account. However, the performance of the model is subject to the feature weights and the time consumption on training. In the paper, an efficient feature-weighted SVR is proposed. Firstly, the value constraint of each weight is obtained according to the maximal information coefficient which reveals the relationship between each input feature and output. Then, the constrained particle swarm optimization (PSO) algorithm is employed to optimize the feature weights and the hyperparameters simultaneously. Finally, the optimal weights are used to modify the kernel function. Simulation experiments were conducted on four synthetic datasets and seven real datasets by using the proposed model, classical SVR, and some state-of-the-art feature-weighted SVR models. The results show that the proposed method has the superior generalization ability within acceptable time.
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Zhu, Hancan, i Guanghua He. "Joint Neighboring Coding with a Low-Rank Constraint for Multi-Atlas Based Image Segmentation". Journal of Medical Imaging and Health Informatics 10, nr 2 (1.02.2020): 310–15. http://dx.doi.org/10.1166/jmihi.2020.2884.

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Multi-atlas methods have been successful for solving many medical image segmentation problems. Under the multi-atlas segmentation framework, labels of atlases are first propagated to the target image space with the deformation fields generated by registering atlas images onto a target image, and then these labels are fused to obtain the final segmentation. While many label fusion strategies have been developed, weighting based label fusion methods have attracted considerable attention. In this paper, we first present a unified framework for weighting based label fusion methods. Under this unified framework, we find that most of recent developed weighting based label fusion methods jointly consider the pair-wise dependency between atlases. However, they independently label voxels to be segmented, ignoring their neighboring spatial structure that might be informative for obtaining robust segmentation results for noisy images. Taking into consideration of potential correlation among neighboring voxels to be segmented, we propose a joint coding method (JCM) with a low-rank constraint for the multi-atlas based image segmentation in a general framework that unifies existing weighting based label fusion methods. The method has been validated for segmenting hippocampus from MR images. It is demonstrated that our method can achieve competitive segmentation performance as the state-of-the-art methods, especially when the quality of images is poor.
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Zhao, Qingzhi, Yibin Yao i Wanqiang Yao. "A troposphere tomography method considering the weighting of input information". Annales Geophysicae 35, nr 6 (13.12.2017): 1327–40. http://dx.doi.org/10.5194/angeo-35-1327-2017.

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Abstract. Troposphere tomography measurement using a global navigation satellite system (GNSS) generally consists of several types of input information including the observation equation, horizontal constraint equation, vertical constraint equation, and a priori constraint equation. The reasonable weightings of input information are a prerequisite for ensuring the reliability of the adjustment of the parameters. This forms the focus of this research, which tries to determine the weightings, including the observations, for the same type of equation and the optimal weightings for different types of equations. The optimal weightings of the proposed method are realized on the basis of the stable equilibrium relationship between different types of a posteriori unit weight variances, which are capable of adaptively adjusting the weightings for different types of equations and enables the ratio between the two arbitrary a posteriori unit weight variances to tend to unity. A troposphere tomography experiment, which was used to consider these weightings, was implemented using global positioning system (GPS) data from the Hong Kong Satellite Positioning Reference Station Network (SatRef). Numerical results show the applicability and stability of the proposed method for GPS troposphere tomography assessment under different weather conditions. In addition, the root mean square (RMS) error in the water vapor density differences between tomography-radiosonde and tomography-ECMWF (European Centre for Medium-Range Weather Forecasts) are 0.91 and 1.63 g m−3, respectively, over a 21-day test.
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Kimmel, Melanie, Jannick Pfort, Jan Wöhlke i Sandra Hirche. "Shared invariance control for constraint satisfaction in multi-robot systems". International Journal of Robotics Research 38, nr 10-11 (12.08.2019): 1268–85. http://dx.doi.org/10.1177/0278364919867133.

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In systems involving multiple intelligent agents, e.g. multi-robot systems, the satisfaction of environmental, inter-agent, and task constraints is essential to ensure safe and successful task execution. This requires a constraint enforcing control scheme, which is able to allocate and distribute the required evasive control actions adequately among the agents, ideally according to the role of the agents or the importance of the executed tasks. In this work, we propose a shared invariance control scheme in combination with a suitable agent prioritization to control multiple agents safely and reliably. Based on the projection of the constraints into the input spaces of the individual agents using input–output linearization, shared invariance control determines constraint enforcing control inputs and facilitates implementation in a distributed manner. In order to allow for shared evasive actions, the control approach introduces weighting factors derived from a two-stage prioritization scheme, which allots the weights according to a variety of factors such as a fixed task priority, the number of constraints affecting each agent or a manipulability measure. The proposed control scheme is proven to guarantee constraint satisfaction. The approach is illustrated in simulations and an experimental evaluation on a dual-arm robotic platform.
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Yin, Fulian, Lu Lu, Jianping Chai i Yanbing Yang. "Combination Weighting Method Based on Maximizing Deviations and Normalized Constraint Condition". International Journal of Security and Its Applications 10, nr 2 (28.02.2016): 39–50. http://dx.doi.org/10.14257/ijsia.2016.10.2.04.

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Rozprawy doktorskie na temat "Constraint weighting"

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Thornton, John Richard, i n/a. "Constraint Weighting Local Search for Constraint Satisfaction". Griffith University. School of Computing and Information Technology, 2000. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20050901.142439.

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One of the challenges for the constraint satisfaction community has been to develop an automated approach to solving Constraint Satisfaction Problems (CSPs) rather than creating specific algorithms for specific problems. Much of this work has concentrated on the development and improvement of general purpose backtracking techniques. However, the success of relatively simple local search techniques on larger satisfiability problems [Selman et a!. 1992] and CSPs such as the n-queens [Minton et al. 1992] has caused interest in applying local search to constraint satisfaction. In this thesis we look at the usefulness of constraint weighting as a local search technique for constraint satisfaction. The work is based on the clause weighting ideas of Selman and Kautz [1993] and Moths [1993] and applies, evaluates and extends these ideas from the satisfiability domain to the more general domain of CSPs. Specifically, the contributions of the thesis are: 1. The introduction of a local search taxonomy. We examine the various better known local search techniques and recognise four basic strategies: restart, randomness, memory and weighting. 2. The extension of the CSP modelling framework. In order to represent and efficiently solve more realistic problems we extend the C SP modelling framework to include array-based domains and array-based domain use constraints. 3. The empirical evaluation of constraint weighting. We compare the performance of three constraint weighting strategies on a range of CSP and satisflability problems and with several other local search techniques. We find that no one technique dominates in all problem domains. 4. The characterisation of constraint weighting performance. Based on our empirical study we identiIS' the weighting behaviours and problem features that favour constrtt weighting. We conclude weighting does better on structured problems where the algorithm can recognise a harder sub-group of constraints. 5. The extension of constraint weighting. We introduce an efficient arc weighting algorithm that additionally weights connections between constraints that are simultaneously violated at a local minimum. This algorithm is empirically shown to outperform standard constraint weighting on a range of CSPs and within a general constraint solving system. Also we look at combining constraint weighting with other local search heuristics and find that these hybrid techniques can do well on problems where the parent algorithms are evenly matched. 6. The application of constraint weighting to over constrained domains. Our empirical work suggests constraint weighting does well for problems with distinctions between constraint groups. This led us to investigate solving real-world over constrained problems with hard and soft constraint groups and to introduce two dynamic constraint weighting heuristics that maintain a distinction between hard and soft constraint groups while still adding weights to violated constraints in a local minimum. In an empirical study, the dynamic schemes are shown to outperform other fixed weighting and non-weighting systems on a range of real world problems. In addition, the performance of weighting is shown to degrade less severely when soft constraints are added to the system, suggesting constraint weighting is especially applicable to realistic, hard and soft constraint problems
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Rui-BinZhang i 張睿彬. "Tracker-Design for the Unknown System with an Input-Output Feed-Through Term and Constraints: An Adaptive Mechanism for Tuning Weighting Matrices". Thesis, 2014. http://ndltd.ncl.edu.tw/handle/9qxngz.

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碩士
國立成功大學
電機工程學系
102
An observer/Kalman filter identification (OKID) method-based adaptive mechanism for tuning weighting matrices of multi-objective cost function is newly proposed in this thesis. An efficient algorithm is newly derived for the new tracker-design for the unknown system with an input-output feed-through term and input/state/output constraints. First, the unknown linear/nonlinear system containing an input-output feed-through term is identified by the observer/Kalman filter identification (OKID) method to have the equivalent mathematical model, then the controller is analyzed and designed by the equivalent mathematical model. The linear analogue quadratic performance index is modified to contain the term of input, state, and output constraints. The linear analogue quadratic performance index with input, state, and output constraints can be directly discretized into an equivalent discrete function, so that the obtained quadratic sub-optimal digital tracker can preserve the performance of the linear analogue quadratic performance index. In order to make the exceeding input, state and output update quickly and accurately, an OKID-based adaptive mechanism for tuning the weighting matrices is constructed. Finally, an OKID-based adaptive mechanism for tuning weighting matrices of the new tracker-design for the unknown system with an input-output feed-through term and input, state, and output constraints is proposed.Examples show the usefulness of the proposed design.
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Książki na temat "Constraint weighting"

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Jarosz, Gaja. Learning with Violable Constraints. Redaktorzy Jeffrey L. Lidz, William Snyder i Joe Pater. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199601264.013.30.

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This chapter provides a broad overview of recent research on constraint-based learning of phonology. The review covers the major results and the contributions of a wide range of approaches, comparing the computational properties and learning implications of these theories. Specifically, the review encompasses learning in classic OT as well as learning in related frameworks that formalize constraint interaction as (probabilistic) weighting or ranking. The learning problem is decomposed into several subproblems that highlight the complexity of the learning problem facing the child learner. The discussion emphasizes the challenges posed by these various subproblems, the insights and differences of the proposed solutions, and the outstanding questions that require further research.
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Części książek na temat "Constraint weighting"

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Thornton, John, i Abdul Sattar. "Dynamic constraint weighting for over-constrained problems". W PRICAI’98: Topics in Artificial Intelligence, 377–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0095285.

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Thornton, John, i Abdul Sattar. "On the Behavior and Application of Constraint Weighting". W Principles and Practice of Constraint Programming – CP’99, 446–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-540-48085-3_32.

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Kotsakis, C. "Reference Station Weighting and Frame Optimality in Minimally Constrained Networks". W VIII Hotine-Marussi Symposium on Mathematical Geodesy, 221–26. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/1345_2015_139.

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Förstner, Wolfgang. "On Weighting and Choosing Constraints for Optimally Reconstructing the Geometry of Image Triplets". W Lecture Notes in Computer Science, 669–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45053-x_43.

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"22. Linear Least Squares with Linear Equality Constraints by Weighting". W Solving Least Squares Problems, 148–57. Society for Industrial and Applied Mathematics, 1995. http://dx.doi.org/10.1137/1.9781611971217.ch22.

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Mathys, Christoph. "How Could We Get Nosology from Computation?" W Computational Psychiatry. The MIT Press, 2016. http://dx.doi.org/10.7551/mitpress/9780262035422.003.0007.

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Psychiatry has found it difficult to develop a nosology that allows for the targeted treatment of disorders of the mind. This article sets out a possible way forward: harnessing systems theory to provide the conceptual constraints needed to link clinical phenomena with neurobiology. This approach builds on the insight that the mind is a system which, to regulate its environment, needs to have a model of that environment and needs to update predictions about it using the rules of inductive logic. It can be shown that Bayesian inference can be reduced to updating beliefs based on precision-weighted prediction errors, where a prediction error is the difference between actual and predicted input, and precision is the confidence associated with the input prediction. Precision weighting of prediction errors entails that a given discrepancy between outcome and prediction means more, and leads to greater belief updates, the more confidently the prediction was made. This provides a conceptual framework linking clinical experience with the pathophysiology underlying disorders of the mind. Limitations of this approach are discussed and ways to work around them illustrated. Initial steps and possible future directions toward a nosology based on failures of precision weighting are discussed.
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dos Santos, José Gomes, Liliana Raquel Simões Azevedo i Luís Carlos Roseiro Leitão. "Spotting Premium Hot Spots for Urban Tourism Based on Facebook and Foursquare Data Using VGI and GIS". W Methods and Applications of Geospatial Technology in Sustainable Urbanism, 159–86. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2249-3.ch006.

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Spatial modeling always involves choices. The existence of constraints, the uncertainty and even the reliability of the data, the purposes and the applications of the studies make these reflections a kind of guiding compass for GIS analysts. Building on a previous exercise of data acquisition (check-ins) based on two digital social networks (DSN – Facebook and Foursquare) and on the awareness of the use of volunteered geographic information (VGI) generated by tourists through DSN, this work aims to evaluate the contribution of spatial analysis applied to urban tourism in the “Alta and University of Coimbra” area. Concepts and procedural tasks related to density determination, cluster analysis, and identification of patterns have thus been implemented with the purpose of evaluating and comparing the results obtained through the application of two techniques of spatial analysis, kernel density estimation (KDE) and optimized hot spot analysis (OHSA) and inverse distance weighting (IDW) interpolation.
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Streszczenia konferencji na temat "Constraint weighting"

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Wattez, Hugues, Christophe Lecoutre, Anastasia Paparrizou i Sebastien Tabary. "Refining Constraint Weighting". W 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2019. http://dx.doi.org/10.1109/ictai.2019.00019.

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Taniguchi, Naokazu, Chen-Fen Huang, Xiao-Hua Zhu i Chuanzheng Zhang. "Horizontal ocean current tomography with iterative model weighting constraint". W OCEANS 2014 - TAIPEI. IEEE, 2014. http://dx.doi.org/10.1109/oceans-taipei.2014.6964415.

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Wang, J. Y., i J. K. Wu. "Nonholonomic Constrain Models for Recursive Dynamic Analysis". W ASME 1990 Design Technical Conferences. American Society of Mechanical Engineers, 1990. http://dx.doi.org/10.1115/detc1990-0056.

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Abstract This paper presents mathematic formulations of nonholonomic constrain models, such as differential kinematic constraints, that can be used in dynamic analysis based on recursive formulations. Rolling and rotating between a pair of bodies are considered in the formulation. These mathematic models include state vector notations, cut differential kinematic constraints, and the right-hand side of constraint acceleration equations using joint coordinates. In addition, the paper describes the factors that need to be considered in selecting cut-joints in spanning tree analysis, that is needed for the recursive formulation. The weighting factors of certain nonholonomic constraints are suggested. In addition to current dynamic formulations, this study provides a systematic method to formulate the dynamic analysis for the mechanical systems with nonholonomic constraints. The results of applying this new formulation to several well-known examples are presented.
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Ling, Yongguo, Zhiming Luo, Yaojin Lin i Shaozi Li. "A Multi-Constraint Similarity Learning with Adaptive Weighting for Visible-Thermal Person Re-Identification". W Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/117.

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The challenges of visible-thermal person re-identification (VT-ReID) lies in the inter-modality discrepancy and the intra-modality variations. An appropriate metric learning plays a crucial role in optimizing the feature similarity between the two modalities. However, most existing metric learning-based methods mainly constrain the similarity between individual instances or class centers, which are inadequate to explore the rich data relationships in the cross-modality data. Besides, most of these methods fail to consider the importance of different pairs, incurring an inefficiency and ineffectiveness of optimization. To address these issues, we propose a Multi-Constraint (MC) similarity learning method that jointly considers the cross-modality relationships from three different aspects, i.e., Instance-to-Instance (I2I), Center-to-Instance (C2I), and Center-to-Center (C2C). Moreover, we devise an Adaptive Weighting Loss (AWL) function to implement the MC efficiently. In the AWL, we first use an adaptive margin pair mining to select informative pairs and then adaptively adjust weights of mined pairs based on their similarity. Finally, the mined and weighted pairs are used for the metric learning. Extensive experiments on two benchmark datasets demonstrate the superior performance of the proposed over the state-of-the-art methods.
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Jin, Ming, Youming Li i Xiaoli Wang. "Joint Thinned Array and Weighting Design Method to Reduce Sidelobe Using L1 Norm Constraint". W 2011 7th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2011. http://dx.doi.org/10.1109/wicom.2011.6040081.

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An, J. F. "Optimal pre-weighting scheme for spatially correlated MIMO-OFDM wireless system with subcarrier cluster constraint". W 2013 International Conference on Computing, Networking and Communications (ICNC 2013). IEEE, 2013. http://dx.doi.org/10.1109/iccnc.2013.6504173.

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Doki, Hitoshi, Kazuhiko Hiramoto, Jun Kaido i Robert E. Skelton. "Actuator Selection for Vibration Control With Control Energy Constraints". W ASME 1997 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/detc97/vib-3832.

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Abstract This paper deals with a sensor/actuator placement problem in design of active vibration control systems for flexible structures. This problem is formulated as a minimization problem of the total energy which is defined as a sum of a kinetic and strain energy in a controlled structure with a constraint of control effort. The inequality constraint on the variance of the closed-loop control effort is adopted to represent the capacity (dynamic range) of the actuator. Using a design algorithm which iteratively tunes the weighting matrix of the quadratic performance index in the LQG problem, the controller which meets these specifications can be synthesized. The optimal location of the sensor/actuator is determined by calculating the total energy for each candidate under several energy constraints of the control effort. The optimal placement of the sensor/actuator depends on the control energy constraint. Simulations and experiments for a cantilevered beam are conducted. These results of the optimization can be used as a guide to the design of active vibration control system.
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Tang, Lei, Wei Xu, Xiaoguang Wang, Xinyu Xiao, Dinghao Dong i Yunhong Zhang. "Predictive Thrust Control without Optimal Weighting Factor Based on Robust Primary Flux Constraint for Linear Induction Machine". W 2021 13th International Symposium on Linear Drives for Industry Applications (LDIA). IEEE, 2021. http://dx.doi.org/10.1109/ldia49489.2021.9505901.

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Aghili, Farhad. "Invariant Modeling of Mechanical Systems in Terms of Quasi-Velocities and Quasi-Forces". W ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86349.

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A gauge-invariant formulation for deriving the dynamic equations of constrained multi-body systems (MBS) in terms of (reduced) quasi–velocities is presented. This formulation does not require any weighting matrix to deal with the gauge-invariance problem when both translational and rotational components are involved in the generalized coordinates or in the constraint equations. Moreover, in this formulation the equations of motion are decoupled from those of constrained force and each system has its own independent input. This allows the possibility to develop a simple force control action that is totally independent from the motion control action facilitating a hybrid force/motion control. Tracking force/motion control of constrained multi-body systems based on a combination of feedbacks on the vectors of the quasi–velocities and the configuration variables are presented.
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Yim, Woosoon, i Sahjendra N. Singh. "End Point Control of Nonlinear Flexible Manipulators With Control Constraint Using SDRE Method". W ASME 2002 International Mechanical Engineering Congress and Exposition. ASMEDC, 2002. http://dx.doi.org/10.1115/imece2002-33126.

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The paper treats the question of end point regulation of multi-link light-weight manipulators using the state dependent Riccati equation (SDRE) method. It is assumed that each link is flexible and deforms when maneuvered. It is well known that end point trajectory control using widely used feedback linearization technique is not possible since the system is nonminimum phase. Furthermore, control saturation is a major problem in controlling nonlinear systems. In this paper, an optimal control problem is formulated for the derivation of control law with and without control constraints on the joint torques and suboptimal control laws are designed using the SDRE method. This design approach is applicable to minimum and as well as nonminimum phase nonlinear systems. For the purpose of control, psuedo joint angles and elastic modes of each link are regulated to their equilibrium values which correspond to the target end point under gravity. Weighting matrices in the quadratic performance index provide flexibility in shaping the psuedo angle and elastic mode trajectories. In the closed-loop system, the equilibrium state is asymptotically stable, and vibration is uppressed. Simulation results are presented for a single link flexible manipulator which shows that in the closed-loop system, end point regulation is accomplished even with hard bounds on the control torque, and that the transient characteristics of the psuedo angles and elastic modes are easily shaped by the choice of the the performance criterion.
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