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检索条件"主题词=Algorithm Design And Analysis"

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Truncated Max-of-Convex Models 30

Truncated Max-of-Convex Models

引用

30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Pansari, Pankaj Kumar, M. Pawan Univ Oxford Alan Turing Inst Oxford England

ISBN: (纸本)9781538604571

Truncated convex models (TCM) are a special case of pairwise random fields that have been widely used in computer vision. However, by restricting the order of the potentials to be at most two, they fail to capture useful image statistics. We propose a natural generalization of TCM to high-order random fields, which we call truncated max-of-convex models (TMCM). The energy function of TMCM consists of two types of potentials: (i) unary potential, which has no restriction on its form;and (ii) clique potential, which is the sum of the m largest truncated convex distances over all label pairs in a clique. The use of a convex distance function encourages smoothness, while truncation permits discontinuities in the labeling. By using m > 1, TMCM provides robustness towards errors in the definition of the cliques. To minimize the energy function of a TMCM over all possible labelings, we design an efficient st-MINCUT based range expansion algorithm. We prove the accuracy of our algorithm by establishing strong multiplicative bounds for several special cases of interest. Using standard real data sets, we demonstrate the benefit of our high-order TMCM over pairwise TCM, as well as the benefit of our range expansion algorithm over other st-MINCUT based approaches.

关键词： Labeling Random variables Robustness Computational modeling algorithm design and analysis Approximation algorithms Optimization

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Early Detection of Diseases using Electronic Health Records Data and Covariance-Regularized Linear Discriminant analysis 4

Early Detection of Diseases using Electronic Health Records ...

引用

4th IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)

作者： Bian, Jiang Barnes, Laura E. Chen, Guanling Xiong, Haoyi Missouri Univ Sci & Technol Rolla MO 65409 USA Univ Virginia Dept Syst & Informat Engn Charlottesville VA 22903 USA Univ Massachusetts Lowell Dept Comp Sci Lowell MA USA

ISBN: (纸本)9781509041794

The availability of Electronic Health Records (EHR) in health care settings provides terrific opportunities for early detection of patients' potential diseases. While many data mining tools have been adopted for EHR-based disease early detection, Linear Discriminant analysis (LDA) is one of the most widely-used statistical prediction methods. To improve the performance of LDA for early detection of diseases, we proposed to leverage CRDA - Covariance-Regularized LDA classifiers on top of diagnosis-frequency vector data representation. Specifically, CRDA employs a sparse precision matrix estimator derived based on graphical lasso to boost the accuracy of LDA classifiers. algorithm analysis demonstrates that the error bound of graphical lasso estimator can intuitively lower the misclassification rate of LDA models. We performed extensive evaluation of CRDA using a large-scale real-world EHR dataset - CHSN for predicting mental health disorders (e.g., depression and anxiety) in college students from 10 US universities. We compared CRDA with other regularized LDA and downstream classifiers. The result shows CRDA outperforms all baselines by achieving significantly higher accuracy and F1 scores.

关键词： Diseases Training Covariance matrices Support vector machines Testing Sensitivity algorithm design and analysis

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Transmit Signal design for MIMO Wiretap Channels with Statistical CSI and Arbitrary Inputs 28

Transmit Signal Design for MIMO Wiretap Channels with Statis...

引用

28th Annual IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

作者： Aghdam, Sina Rezaei Duman, Tolga M. Bilkent Univ Dept Elect & Elect Engn TR-06800 Ankara Turkey

ISBN: (纸本)9781538635315

We propose transmit optimization techniques for multi-input multi-output (MIMO) wiretap channels with statistical channel state information (CSI) at the transmitter. We consider doubly correlated channels towards the legitimate receiver and the eavesdropper. The aim is to maximize the secrecy rates using the knowledge of the channel correlation matrices. We develop gradient-descent based optimization algorithms for obtaining the optimal transmit signals for both Gaussian and finite-alphabet inputs. Furthermore, we introduce a joint precoder and artificial noise (AN) design scheme. We demonstrate the efficacy of the proposed schemes via numerical examples.

关键词： Optimization Receivers Transmitters algorithm design and analysis Covariance matrices Correlation Signal design

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Approximate Computing Techniques for Iterative Graph algorithms 24

Approximate Computing Techniques for Iterative Graph Algorit...

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IEEE 24th International Conference on High Performance Computing Workshops (HiPCW)

作者： Panyala, Ajay Subasi, Omer Halappanavar, Mahantesh Kalyanaraman, Ananth Chavarria-Miranda, Daniel Krishnamoorthy, Sriram Pacific Northwest Natl Lab Richland WA 99352 USA Washington State Univ Pullman WA 99164 USA Trovares Inc Seattle WA USA

ISBN: (纸本)9781538622933

Approximate computing enables processing of large-scale graphs by trading off quality for performance. Approximate computing techniques have become critical not only due to the emergence of parallel architectures but also due to the availability of large scale datasets enabling data-driven discovery. Using two prototypical graph algorithms, PageRank and community detection, we present several approximate computing heuristics to scale the performance with minimal loss of accuracy. We present several heuristics including loop perforation, data caching, incomplete graph coloring and synchronization, and evaluate their efficiency. We demonstrate performance improvements of up to 83% for PageRank and up to 450x for community detection, with low impact on accuracy for both the algorithms. We expect the proposed approximate techniques will enable scalable graph analytics on data of importance to several applications in science and their subsequent adoption to scale similar graph algorithms.

关键词： Approximation algorithms Approximate computing Iterative algorithms Color Convergence algorithm design and analysis Heuristic algorithms

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Control of a Quadrotor Helicopter using a Stable Introspective-CMAC Adaptive Control 56

Control of a Quadrotor Helicopter using a Stable Introspecti...

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56th Annual IEEE Conference on Decision and Control (CDC)

作者： Macnab, C. J. B. Univ Calgary Dept Elect & Comp Engn Calgary AB Canada

ISBN: (纸本)9781509028733

The Cerebellar Model Articulation Controller (CMAC) neural network utilizes hypercube basis function domains, called cells. Because of the local nature of the cells the CMAC exhibits more weight drift (also called parameter drift or overlearning) compared to other types of neural networks. Typical robust weight update law modifications designed to prevent drift, like deadzone or e-modification, often sacrifice performance. This paper proposes a solution to this problem by using two CMACs in the control law, referred to as a performance CMAC and a robust CMAC. The performance CMAC reduces the state error to a small value during initial training, and then turns off its weight updates according to the output of a decision algorithm in order to capture the best performance. One proposed algorithm simply turns off the training of a weight after its cell has been activated for a certain period of time. A more advanced method, deemed the introspective algorithm, stops training in a cell when it appears that the average error measured over sequential cell domains is no longer being reduced by the weight update in a cell. The robust CMAC trains in parallel using a conservative e-modification algorithm, continuing to provide robustness after the performance CMAC has stopped training. Lyapunov methods show a guarantee of uniformly ultimately bounded signals. Simulations with a quadrotor helicopter demonstrate the superior performance of the proposed method over e-modification, deadzone, and a PID control.

关键词： Robustness Training Adaptive control Neural networks Measurement uncertainty Weight measurement algorithm design and analysis

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Fairness-aware Dynamic Rate Control and Flow Scheduling for Network Function Virtualization 25

Fairness-aware Dynamic Rate Control and Flow Scheduling for ...

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25th IEEE/ACM International Symposium on Quality of Service (IWQoS)

作者： Tao, Sheng Gu, Lin Zeng, Deze Jin, Hai Hu, Kan Huazhong Univ Sci & Technol Sch Comp Sci & Technol Cluster & Grid Comp Lab Serv Comp Technol & Syst Lab Wuhan Hubei Peoples R China China Univ Geosci Sch Comp Sci Wuhan Hubei Peoples R China

ISBN: (纸本)9781538627044

By softwarizing traditional dedicated hardware based functions to virtualized network functions (VNFs) that can run on standard commodity servers, network function virtualization (NFV) technology promises high efficiency, flexibility and scalability. To NFV service providers, one primary concern is to maximize network throughput and reduce service time. To reach this goal, two main challenges should be tackled: 1) how to schedule the unpredictable and burst network flows;2) how to fairly allocate resources between various flows with different resource requirements. In this paper, we are motivated to investigate a throughput maximization problem with joint consideration of fairness between multiple flows using a discrete time queuing model. By taking advantages of Lyapunov optimization techniques, we propose a low-complexity online distributed algorithm that can achieve arbitrary optimal utility with different fairness levels by tuning the fairness bias. The high efficiency of our proposal is validated by both theoretical analysis and extensive simulation studies.

关键词： Radio frequency Throughput Heuristic algorithms algorithm design and analysis Artificial neural networks Dynamic scheduling Optimization

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A Game-Theoretic Defense Against Data Poisoning Attacks in Distributed Support Vector Machines 56

A Game-Theoretic Defense Against Data Poisoning Attacks in D...

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56th Annual IEEE Conference on Decision and Control (CDC)

作者： Zhang, Rui Zhu, Quanyan NYU Dept Elect & Comp Engn Brooklyn NY 11201 USA

ISBN: (纸本)9781509028733

With a large number of sensors and control units in networked systems, distributed support vector machines (DSVMs) play a fundamental role in scalable and efficient multi-sensor classification and prediction tasks. However, DSVMs are vulnerable to adversaries who can modify and generate data to deceive the system to misclassification and misprediction. This work aims to design defense strategies for DSVM learner against a potential adversary. We use a game-theoretic framework to capture the conflicting interests between the DSVM learner and the attacker. The Nash equilibrium of the game allows predicting the outcome of learning algorithms in adversarial environments, and enhancing the resilience of the machine learning through dynamic distributed algorithms. We develop a secure and resilient DSVM algorithm with rejection method, and show its resiliency against adversary with numerical experiments.

关键词： Support vector machines Games Sensor fusion algorithm design and analysis Game theory Training

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Cloud-distributed computational experiments for combinatorial optimization 40

Cloud-distributed computational experiments for combinatoria...

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40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

作者： Brcic, Mario Hlupic, Nikica Fac Elect Engn & Comp Zagreb Croatia

ISBN: (纸本)9789532330922

The development of optimization algorithms for combinatorial problems is a complicated process, both guided and validated by the computational experiments over the different scenarios. Since the number of experiments can be very large and each experiment can take substantial execution time, distributing the load over the cloud speeds up the whole process significantly. In this paper we present the system used for experimental validation and comparison of stochastic combinatorial optimization algorithms, applied in the specific case of project scheduling problems.

关键词： Databases Optimization Cloud computing algorithm design and analysis Computer architecture Scheduling Tools

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An Efficient Backtracking-based Approach to Turn-constrained Path Planning for Aerial Mobile Robots

An Efficient Backtracking-based Approach to Turn-constrained...

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European Conference on Mobile Robots (ECMR)

作者： Sharma, Hrishikesh Sebastian, Tom Balamuralidhar, P. Tata Consultancy Serv Ltd Embedded Syst & Robot Res Lab Bombay Maharashtra India

ISBN: (纸本)9781538610961

Many important classes of civilian applications of Unmanned Aerial Vehicles, such as the class of remote monitoring of long linear infrastructures e.g. power grid, gas pipeline etc. entail usage of fixed-wing vehicles. Such vehicles are known to be constrained with restricted angular movement. Similarly, mobile robots such as car robots or tractor-trailer robots are also known to entail such constraint. The algorithms known so far require a lot of preprocessing for turn constraint. In this paper, we introduce a novel algorithm for turn angle-constrained path planning. The proposed algorithm uses a greedy backtracking strategy to satisfy the constraint, which minimizes the amount of backtracking involved. By further constructing an efficient depth-first brute-force algorithm for path planning and comparing against its performance, we see an improvement in convergence performance by a factor of at least 10x. Further, compared to recent LIAN suite of path-planning algorithm, our algorithm exhibits much reduced discretization offset/error with respect to shortest path length. We believe that this algorithm will form an useful stepping stone towards evolution of better path planning algorithm for specific mobile robots such as UAVs.

关键词： algorithm design and analysis Path planning Planning Turning Unmanned aerial vehicles Two dimensional displays Approximation algorithms

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A Novel Power Flow algorithm for AC Microgrids Based on Time Domain Iteration 43

A Novel Power Flow Algorithm for AC Microgrids Based on Time...

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43rd Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Zhu, Yixin Wang, Tao Xiong, Liansong Yang, Ping Xu, ZhiRong Jiangnan Univ Inst Automat Wuxi Peoples R China Nanjing Inst Technol Sch Automat Nanjing Jiangsu Peoples R China South China Univ Technol Sch Elect Power Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538611272

To manage the microgrid effectively, it is important to get the power information among multiple distributed generation (DG) units. However, when the droop control method is adopted, as the corresponding DG units cannot be simply modeled as VF or PQ buses, the conventional power flow algorithm may become inapplicable. To solve this issue, a time domain iteration (TDI) based power flow algorithm is hereby proposed. Firstly, a microgrid model is prepared, containing a network model and several DG unit models. Then, the proposed TDI is executed for power flow calculation, which mimics the real-time operation of microgrids. In each iteration, the DG unit models input voltages and currents to the network model;then the network model changes its state accordingly and feeds related parameters back to those DG unit models. As the DG unit models simulate the behavior of actual DG units, the proposed algorithm is not limited to the droop control governed microgrids. Moreover, as the TDI has definite physical meaning, convergence problems can be avoided. Finally, the validity of the proposed power flow algorithm is verified through the Matlab simulation results from an 8 -bus microgrid system.

关键词： Microgrids Mathematical model Voltage control Reactive power algorithm design and analysis Load modeling Time-domain analysis

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