In this paper, an optimal design method of SR machine for a hydraulic pump system is researched. In order to get a proper efficiency and output power of hydraulic pump system in the restricted outer dimension, dominan...
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ISBN:
(纸本)9780780397163
In this paper, an optimal design method of SR machine for a hydraulic pump system is researched. In order to get a proper efficiency and output power of hydraulic pump system in the restricted outer dimension, dominant design parameters of SR machine are learned by genetic algorithm with CAD program. The genetic algorithm is used to select the stator/rotor pole are and the switching on/off angle of SR machine to improve the operating efficiency. Since a modified CAD program designs dominant parameters from genetic algorithm and returns the calculation results to genetic algorithm routine, all the design procedure is automatically implemented exception of proper fitness function design. In order to verify the proposed design method, the prototype SIR machine with parameters determined by genetic algorithm is tested, and test results show high efficiency at rated output power of hydraulic pump system.
Planning an itinerary when traveling to a city involves substantial effort in choosing Points-of-Interest (POIs), deciding in which order to visit them, and accounting for the time it takes to visit each POI and trans...
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ISBN:
(纸本)9781424489589
Planning an itinerary when traveling to a city involves substantial effort in choosing Points-of-Interest (POIs), deciding in which order to visit them, and accounting for the time it takes to visit each POI and transit between them. Several online services address different aspects of itinerary planning but none of them provides an interactive interface where users give feedbacks and iteratively construct their itineraries based on personal interests and time budget. In this paper, we formalize interactive itinerary planning as an iterative process where, at each step: (1) the user provides feedback on POIs selected by the system, (2) the system recommends the best itineraries based on all feedback so far, and (3) the system further selects a new set of POIs, with optimal utility, to solicit feedback for, at the next step. This iterative process stops when the user is satisfied with the recommended itinerary. We show that computing an itinerary is NP-complete even for simple itinerary scoring functions, and that POI selection is NP-complete. We develop heuristics and optimizations for a specific case where the score of an itinerary is proportional to the number of desired POIs it contains. Our extensive experiments show that our algorithms are efficient and return high quality itineraries.
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 E...
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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. algorithmanalysis 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.
Today, designers of network processors strive to keep the packet reception and transmission orders identical, and therefore avoid any possible out-of-order transmission. However, the development of new features in adv...
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ISBN:
(纸本)9781479916337
Today, designers of network processors strive to keep the packet reception and transmission orders identical, and therefore avoid any possible out-of-order transmission. However, the development of new features in advanced network processors has resulted in increasingly parallel architectures and increasingly heterogeneous packet processing times, leading to large reordering delays. In this paper, we introduce novel scalable scheduling algorithms for preserving flow order in parallel multi-core network processors. We show how these algorithms can reduce reordering delay while adapting to any load-balancing algorithm and keeping a low implementation complexity overhead. To do so, we use the observation that all packets in a given flow have similar processing requirements and can be described with a constant number of logical processing phases. We further define three possible knowledge frameworks of the time when a network processor learns about these logical phases, and deduce appropriate algorithms for each of these frameworks.
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 use...
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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.
We consider a heuristic Bayesian algorithm as a model of human decision making in multi-armed bandit problems with Gaussian rewards. We derive a novel upper bound on the Gaussian inverse cumulative distribution functi...
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ISBN:
(纸本)9783952426913
We consider a heuristic Bayesian algorithm as a model of human decision making in multi-armed bandit problems with Gaussian rewards. We derive a novel upper bound on the Gaussian inverse cumulative distribution function and use it to show that the algorithm achieves logarithmic regret. We extend the algorithm to allow for stochastic decision making using Boltzmann action selection with a dynamic temperature parameter and provide a feedback rule for tuning the temperature parameter such that the stochastic algorithm achieves logarithmic regret. The stochastic algorithm encodes many of the observed features of human decision making.
A detailed simulative and experimental analysis of different CPE schemes for 64-QAM systems is presented. The best compromise between linewidth tolerance and complexity is achieved using a recently proposed multi-stag...
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ISBN:
(纸本)9781479930661
A detailed simulative and experimental analysis of different CPE schemes for 64-QAM systems is presented. The best compromise between linewidth tolerance and complexity is achieved using a recently proposed multi-stage architecture, based on a modification of the standard V&V algorithm.
To solve the problems such as low global search capability and insufficient diversity of Pareto optimal set existed in MOPSO, a multiobjective particle swarm optimization algorithm based on crowding distance sorting i...
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ISBN:
(纸本)9781424438655
To solve the problems such as low global search capability and insufficient diversity of Pareto optimal set existed in MOPSO, a multiobjective particle swarm optimization algorithm based on crowding distance sorting is proposed. An external population is preserved to store the nondominated individuals during the evolution process. The shrink of the external population is achieved based on individuals' crowding distance sorting by descending order, which deleting the redundant individuals in the crowding area. An individual with relatively big crowding distance is selected as the global best to lead the particles evolving to the disperse region. The dominant relation between individuals is compared with the constraint Pareto dominance to embody the constraints without external parameters. The experiments of six standard unconstraint test problems illustrate that the new algorithm is competitive with NSGA-II and SPEA2 in terms of converging to the true Pareto front and maintaining the diversity of the population. The effectiveness of the algorithm for constraint problems is proved by solving three constraint test problems. Moreover, the best value ranges of mutation rate and inertia weight are analyzed by numerical experiments to guarantee the steady convergence of the algorithm.
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 ...
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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.
Navigation in dynamic or unknown environment is a challenge for autonomous vehicles because of the limited range of sensors and not accurate maps. In this paper, we analyze three conditions on the unobserved and uncer...
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ISBN:
(纸本)9781479974092
Navigation in dynamic or unknown environment is a challenge for autonomous vehicles because of the limited range of sensors and not accurate maps. In this paper, we analyze three conditions on the unobserved and uncertainty environment during navigation. They are "known space", "free space", and "unknown space". For the dynamic environment, we derive an algorithm to correct the false obstacles in the map when a conventional path planning is stuck. For the unknown environment, we derive novel algorithms and compare them with the classical approaches under free space condition. Finally we use Monte Carlo method to evaluate the performance of these algorithms. Experimental results show that our conditions based algorithms are better than the others.
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