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2010 49th IEEE Conference on Decision and Control

作者： Tobias Kloiber Guido Koch Boris Lohmann Institute of Automatic Control Technische Universitat Munchen 85748 Garching Germany

ISBN: (纸本)9781424477456

In this paper a new control approach for active vehicle suspensions based on a modified optimal control problem is presented, which considers the nonlinear damper characteristic of a vehicle suspension setup. In this context a new method for the systematic construction of a control Lyapunov function is presented, that is applicable to a class of nonlinear systems. The states that are required by the controller are estimated from the available measurement signals using a nonlinear Kalman filter concept recently presented by the authors. In order to achieve the best possible performance with respect to the conflicting objectives passenger comfort, ride safety and suspension deflection, the controller parameters are determined by means of a multiobjective genetic optimization algorithm. The potential of the controller is demonstrated by comparing it to a conventional linear quadratic regulator. The concept is validated on a quarter-vehicle test rig using measurements of real road profiles as disturbance input.

关键词： Automobile suspensions Controllers Liapunov function Suspension System Active Suspension Systems Optimal control nonlinearity optimization algorithms

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Facial Expression Intensity Estimation Using Ordinal Information

Facial Expression Intensity Estimation Using Ordinal Informa...

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Rui Zhao Quan Gan Shangfei Wang Qiang Ji Department of Electrical Computer & Systems Engineering Rensselaer Polytechnic Institute School of Computer Science and Technology University of Science and Technology of China

ISBN: (纸本)9781467388528

Previous studies on facial expression analysis have been focused on recognizing basic expression categories. There is limited amount of work on the continuous expression intensity estimation, which is important for detecting and tracking emotion change. Part of the reason is the lack of labeled data with annotated expression intensity since expression intensity annotation requires expertise and is time consuming. In this work, we treat the expression intensity estimation as a regression problem. By taking advantage of the natural onset-apex-offset evolution pattern of facial expression, the proposed method can handle different amounts of annotations to perform frame-level expression intensity estimation. In fully supervised case, all the frames are provided with intensity annotations. In weakly supervised case, only the annotations of selected key frames are used. While in unsupervised case, expression intensity can be estimated without any annotations. An efficient optimization algorithm based on Alternating Direction Method of Multipliers (ADMM) is developed for solving the optimization problem associated with parameter learning. We demonstrate the effectiveness of proposed method by comparing it against both fully supervised and unsupervised approaches on benchmark facial expression datasets.

关键词： ANNOTATIONS EXPRESSION ANALYSIS Facial Expression Parameter Learning optimization algorithms

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Multi-objective Design of a Magnetic Fluid Hyperthermia Device

Multi-objective Design of a Magnetic Fluid Hyperthermia Devi...

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Annual Conference of the IEEE Industrial Electronics Society

作者： R. Bertani M. Forzan P. Sgarbossa E. Sieni P. Di Barba F. Spizzo L. Del Bianco Dept. Industrial Engineering University of Padova Padova Italy

ISBN: (纸本)9781479917631

The paper presents the design of a new device to heat a magnetic nanofluid in-vivo. The optimal design of the device has been carried by coupling Finite Elements (FE) solutions and various multi-objective optimization algorithms based on Non-dominated Sorting Genetic algorithms (NSGA). The theoretical heating rate of the nanofluid, as resulting from the analytical solution that describes the heating rate in adiabatic conditions, is compared to the one calculated in a FE model that takes into account more real-life thermal conditions.

关键词： Finite element Magnetic fluid hyperthermia optimization Magnetic fluids FINITE ELEMENT Adiabatic conditions Heating Rate optimization algorithms Fever Device Design GTF2E1 gene Iron multi-objective optimal design

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Energy-Efficient MIMO Precoding and Power Allocation for Device-to-Device Underlay Communication in Cellular Networks

Energy-Efficient MIMO Precoding and Power Allocation for Dev...

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IEEE Vehicular Technology Conference

作者： Bo Chen Minjian Zhao Ming Lei Lei Zhang Department of Information Science and Electronic Engineering Zhejiang University Hangzhou China 310027

ISBN: (纸本)9781479980925

Multiple-input-multiple-output (MIMO) Device-to-Device (D2D) communication underlaying cellular networks can improve user throughout and extend battery life of user equipment. This paper studies MIMO precoding and power allocation schemes for the D2D and cellular uplink communications to improve the energy efficiency of both the D2D and the cellular user (CU). Due to the co-channel interference caused by the shared resources, the precoder design and power allocation of both the D2D transmitter (DTX) and the CU become highly inter-dependent. A distributed cooperative iterative optimization algorithm (DCIOA) is introduced, where either the DTX or the CU cooperatively exchanges the interference information and iteratively updates its optimum precoder and power level. Meanwhile, a centralized stochastic search optimization algorithm using the particle swarm optimization (PSO) method is applied to provide an upper bound. Simulation results show that the performance of the DCIOA is close to that of the centralized algorithm with significant reduction of computation and slight increment of exchange overhead.

关键词： Energy-Efficient D2D Communication MIMO Particle Swarm optimization Distributed Cooperative Iterative optimization Energy efficiency multiple input multiple output systems power splitting Cellular networks Particle swarm optimization Copper Device-to-device communication optimization algorithms prediction coding user facility Battery Life Communication Research Cochannel interference power level

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EVOLUTIONARY FEATURE GENERATION FOR CONTENT-BASED AUDIO CLASSIFICATION AND RETRIEVAL

EVOLUTIONARY FEATURE GENERATION FOR CONTENT-BASED AUDIO CLAS...

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European Signal Processing Conference

作者： Toni Maekinen Serkan Kiranyaz Jenni Pulkkinen Moncef Gabbouj Department of Signal Processing Tampere University of Technology

ISBN: (纸本)9781467310680

Many commonly applied audio features suffer from certain limitations in describing the data content for classification and retrieval purposes. To remedy this drawback, in this paper we propose an evolutionary feature synthesis (EFS) technique, which is applied over traditional audio features to improve their data discrimination power. The underlying evolutionary optimization algorithm performs both feature selection and feature generation in an interleaved manner, optimizing also the dimensionality of the synthesized feature vector. The process is based on multi-dimensional particle swarm optimization (MD PSO) with two additional techniques: the fractional global best formation (FGBF) and simulated annealing (SA). The experimented classification and retrieval performances over a 16-class audio database show improvements of up to 11% when compared to the corresponding performances of the original features.

关键词： Feature generation Particle swarm optimization Neural networks Content-based classification retrieval Audio Eigenvector optimization algorithms Feature extraction audio databases

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The Implicit Bias of Gradient Descent on Separable Multiclass Data

arXiv

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arXiv 2024年

作者： Ravi, Hrithik Scott, Clayton Soudry, Daniel Wang, Yutong University of Michigan United States Technion - Israel Institute of Technology Illinois Institute of Technology United States

Implicit bias describes the phenomenon where optimization-based training algorithms, without explicit regularization, show a preference for simple estimators even when more complex estimators have equal objective values. Multiple works have developed the theory of implicit bias for binary classification under the assumption that the loss satisfies an exponential tail property. However, there is a noticeable gap in analysis for multiclass classification, with only a handful of results which themselves are restricted to the cross-entropy loss. In this work, we employ the framework of Permutation Equivariant and Relative Margin-based (PERM) losses [Wang and Scott, 2024] to introduce a multiclass extension of the exponential tail property. This class of losses includes not only cross-entropy but also other losses. Using this framework, we extend the implicit bias result of Soudry et al. [2018] to multiclass classification. Furthermore, our proof techniques closely mirror those of the binary case, thus illustrating the power of the PERM framework for bridging the binary-multiclass gap. © 2024, CC BY-NC-SA.

关键词： optimization algorithms

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Optimal Trajectory Planning for a Constrained Functional Electrical Stimulation-based Human Walking

Optimal Trajectory Planning for a Constrained Functional Ele...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Nitin Sharma Richard Stein Department of Physiology University of Alberta

ISBN: (纸本)9781424441211

In contrast to the muscle recruitment during voluntary walking, only a limited number of muscles are activated during functional electrical stimulation (FES) based walking. This implies that a trajectory designed or recorded from the normal human walking data may not be the best choice for tracking control. Another major challenge during FES-based walking is the rapid onset of muscle fatigue. Two methods to reduce fatigue during FES-based walking are employing an orthosis and minimizing muscle activations. To deal with these aforementioned challenges, this paper presents firstly a dynamic model representing FES-elicited walking constrained by an orthosis and a walker. Secondly, this paper deals with the design of optimal stimulation and force profiles (instead of gait-trajectories from ablebodied humans) that minimize muscle activations via FES and arm reaction forces from the walker. Ten walking steps are simulated to show the feasibility of the walking model and optimization algorithm.

关键词： trajectory planning walking Orthotic Devices neurophysiological recruitment FES gene muscles optimization algorithms

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Strong Convexity of Sets in Riemannian Manifolds

arXiv

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arXiv 2023年

作者： Scieur, Damien Martínez-Rubio, David Kerdreux, Thomas d’Aspremont, Alexandre Pokutta, Sebastian Samsung SAIL Montreal Canada CNRS & D.I. École Normale Supérieure Paris France Zuse Institute Berlin & Technische Universität Berlin Germany

Curvature properties of convex objects, such as strong convexity, are important in designing and analyzing convex optimization algorithms in the Hilbertian or Riemannian settings. In the case of the Hilbertian setting, strongly convex sets are well studied. Herein, we propose various definitions of strong convexity for uniquely geodesic sets in a Riemannian manifold. We study their relationship, propose tools to determine the geodesic strongly convex nature of sets, and analyze the convergence of optimization algorithms over those sets. In particular, we demonstrate that the Riemannian Frank-Wolfe algorithm enjoys a global linear convergence rate when the Riemannian scaling inequalities hold. © 2023, CC BY.

关键词： optimization algorithms

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Prediction of customer buying nature from frequent itemsets generation using Quine-McCluskey method

Prediction of customer buying nature from frequent itemsets ...

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IET Chennai Fourth International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2013)

作者： M. Krishnamurthy E. Rajalakshmi R Baskaran A. Kannan KCG College of Technology Chennai Tamil Nadu IN Sri Venkateswara College of Engineering Sriperumbudur Tamil Nadu IN Anna University Chennai Chennai Tamil Nadu IN

ISBN: (纸本)9781510804524

Finding frequent itemsets is one of the most important fields of data mining and also finding the rules associated with the itemset is another important ground in association rule mining. The process of finding new techniques to reduce candidate generation in order to generate frequent itemsets efficiently is a challenging task and generating rules in order to know the purchase behaviour of the customer to improve the business. In this paper, an efficient and optimized algorithm called Customer Purchase Behaviour (CPB) has been introduced for finding frequent itemsets using minimum scans, time and memory and the rules are generated. The subset invention process is used for the generation of frequent itemsets which reduces the intermediate tables. This approach reduces main memory requirement since it considers only a small cluster at a time. The purchase behaviour of the customer can be easily judged by using the Quine-McCluskey method. This algorithm is very efficient because of redundancy elimination and rule generation then compared with Apriori, Cluster-Based Bit Vector Mining for Association Rule Generation (CBVAR) and Improved Cluster-based Bit vector mining algorithm for Frequent Itemsets Generation (ICBV).

关键词： Cluster based mining Bit vector mining Redundancy elimination Frequent itemsets generation Customer Purchase Behaviour Quine-McCluskey Itemsets Association rules optimization algorithms learning (artificial intelligence) Information Extraction Rule FINDINGS mining data mining

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Memory Architecture Design for Nano Satellites

Memory Architecture Design for Nano Satellites

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IEEE Aerospace Conference

作者： Nikhil Gupta Bhavya Shahi Manipal Institute of Technology Manipal Karnataka 576104 India

ISBN: (纸本)9781467376778

This paper describes the memory architecture to improve the data transfer and storage in a small satellite. The main *** during the design stage of the architecture is to find a good balance between power consumption, cost, reliability and data processing capability. These variables directly impact each other, and it is important to achieve a suitable balance. For this, a low power flash memory is selected in conjunction with a faster static random access memory to improve the performance of the on-board computer on the satellite. In-built buffers of flash are suitably used to improve system performance. An extensive study of timing requirements to store data in memory is done. A comparison of performance at different voltage levels above the required minimum is done to get a balance between the required speed of programming the memory and power consumption. A highly modular and optimized algorithm is proposed for data transfer and storage which can be easily incorporated into a real time operating system. A method to further save the power is proposed by switching the flash memory to the power saving mode when its usage is not required.

关键词： buffer flash memory memory architecture nanosatellites real time operating systems static random access memory Memory architecture nano satellites Static random access storage flash memories data transmission buffers optimization algorithms

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