Wind noise has detrimental impacts on audio equipment. This paper evaluates the impact of wind noise on the noise control performance of ANC headphones in a variety of windy environments. The findings indicate that th...
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Wind noise has detrimental impacts on audio equipment. This paper evaluates the impact of wind noise on the noise control performance of ANC headphones in a variety of windy environments. The findings indicate that the noise reduction performance of headphones degrades as the wind speed increases. To improve the performance of ANC systems in windy environments, the diffusion filtered -x least mean squares (diff-FxLMS)d* algorithm has been employed that can effectively suppress the wind noise anddisperse the computational burden among nodes across acoustic sensor networks. The theoretical analysis of the diff-FxLMSd* algorithm in wind noise environment indicates that an estimation bias has been introduced in the steady-state solution. Computer simulations using recorded wind noise and measured acoustic channels demonstrate that the diffusiond* algorithm could improve the noise reduction performance in windy environments up to 5 dB approximately.
Loading and unloading operations are frequent and important in the production workshop. Thus, this paper proposes a novel double-row layout problem, which involves the location planning of material loading and unloadi...
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Loading and unloading operations are frequent and important in the production workshop. Thus, this paper proposes a novel double-row layout problem, which involves the location planning of material loading and unloading points of the facility in the layout;It needs to solve the sequence and precise coordinates of facilities with safety clearance to ensure the safety of the layout. Subsequently, a mixed-integer programming model and an improveddiscrete differential evolutiond* algorithm with linear programming are developed to minimize material handling costs. Thed* algorithm includes four efficient operations in optimization: the annealing mech-anism, random strategy, variable neighborhood search strategy, anddouble-threshold termination mode. Thereafter, compared with the computational results of benchmark cases, the validity of the model and the efficiency of thed* algorithm are verified. In the basic problem, the calculation results of thed* algorithm are compared with manyd* algorithms in the literature, and thed* algorithm still performs well. Finally, thed* algorithm is applied to solve the reducer workshop and provides a better layout scheme.
This paper proposes simple anddirect formulation and* algorithms for the probit-based stochastic user equilibrium traffic assignment problem. It is only necessary to account for random variables independent of link fl...
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This paper proposes simple anddirect formulation and* algorithms for the probit-based stochastic user equilibrium traffic assignment problem. It is only necessary to account for random variables independent of link flows by performing a simple transformation of the perceived link travel time with a normal distribution. At every iteration of a Monte-Carlo simulation procedure, the values of the random variables are sampled based on their probability distributions, and then a regular deterministic user equilibrium assignment is carried out to produce link flows. The link flows produced at each iteration of the Monte-Carlo simulation are averaged to yield the final flow pattern. Two test networks demonstrate that the proposed* algorithms and the traditionald* algorithm (the Method of Successive Averages) produce similar results and that the proposed* algorithms can be extended to the computation of the case in which the random error term depends on measured travel time.
New techniques are presented for designing a finite difference domain decompositiond* algorithm for the two- and three-dimensional heat equations. The basic procedure is to define the finite difference schemes at the in...
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New techniques are presented for designing a finite difference domain decompositiond* algorithm for the two- and three-dimensional heat equations. The basic procedure is to define the finite difference schemes at the interface grid points with a smaller time step (delta) over bar (t) over bar and a larger mesh spacing (h) over bar. The stability region of thed* algorithm is expanded Jd(2) times compared with the classical explicit scheme, and a better error bound of the numerical solutions is obtained when (r) over bar = 1/6. Numerical experiments are also presented.
In Part I of our study, stability analysis testing in the reduced space was formulated, and its robustness and efficiency in comparison to the conventional approach was explored. In this paper, we present formulations...
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In Part I of our study, stability analysis testing in the reduced space was formulated, and its robustness and efficiency in comparison to the conventional approach was explored. In this paper, we present formulations including, first, direct solution of the nonlinear equations, and second, minimization of Gibbs free energy for two-phase flash computations in the reduced space. We use variousd* algorithms including the successive substitution (SS), Newton's method, globally convergent modifications of Newton's method (line searches and trust region), and the dominant eigenvalue method (dEM) for direct solution of the nonlinear equations defining two-phase flash and the minimization of Gibbs free energy. We also suggest a criterion based on the tangent-plane-distance (TPd) for the initialization from the equilibrium ratios. The proposed criterion has a significant effect on reducing the number of iterations. The results from variousd* algorithms reveal that the direct solution of the nonlinear equations in the reduced space, combined with the use of the TPd criterion for initialization in the combined SS and Newton's method, can make flash computations extremely efficient. The efficiency and robustness of flash computations in the critical region are especially remarkable.
And* algorithm is presented which can be efficiently applied to a variety of sonar imaging modes including forward looking, side-looking and synthetic aperture sonar. The digital focused beamforming (dFB)d* algorithm for ...
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And* algorithm is presented which can be efficiently applied to a variety of sonar imaging modes including forward looking, side-looking and synthetic aperture sonar. The digital focused beamforming (dFB)d* algorithm for pixel based imaging utilises key attributes of both time and frequency domain techniques. dFB for forward looking imaging is described in detail and adaptation of the basic concept in application to the other imaging modes is outlined. Examples of both simulated and real images are presented.
data similarity (or distance) computation is a fundamental research topic which underpins many high-level applications based on similarity measures in machine learning anddata mining. However, in large-scale real-wor...
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data similarity (or distance) computation is a fundamental research topic which underpins many high-level applications based on similarity measures in machine learning anddata mining. However, in large-scale real-world scenarios, the exact similarity computation has become daunting due to "3V" nature (volume, velocity and variety) of big data. In this case, the hashing techniques have been verified to efficiently conduct similarity estimation in terms of both theory and practice. Currently, MinHash is a popular technique for efficiently estimating the Jaccard similarity of binary sets and furthermore, weighted MinHash is generalized to estimate the generalized Jaccard similarity of weighted sets. This review focuses on categorizing anddiscussing the existing works of weighted MinHashd* algorithms. In this review, we mainly categorize the weighted MinHashd* algorithms into quantization-based approaches, "active index"-based ones and others, and show the evolution and inherent connection of the weighted MinHashd* algorithms, from the integer weighted MinHash ones to the real-valued weighted MinHash ones. Also, we have developed a Python toolbox for thed* algorithms, and released it in our github. We experimentally conduct a comprehensive study of the standard MinHashd* algorithm and the weighted MinHash ones in the similarity estimation error and the information retrieval task.
N-list is a novel data structure proposed in recent years. It has been proven to be very efficient for mining frequent itemsets. In this paper, we present PrePost(+), a high-performanced* algorithm for mining frequent i...
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N-list is a novel data structure proposed in recent years. It has been proven to be very efficient for mining frequent itemsets. In this paper, we present PrePost(+), a high-performanced* algorithm for mining frequent itemsets. It employs N-list to represent itemsets anddirectly discovers frequent itemsets using a set-enumeration search tree. Especially, it employs an efficient pruning strategy named Children-Parent Equivalence pruning to greatly reduce the search space. We have conducted extensive experiments to evaluate PrePost(+) against three state-of-the-artd* algorithms, which are PrePost, FIN, and FP-growth*, on six various real datasets. The experimental results show that PrePost(+) is always the fastest one on all datasets. Moreover, PrePost(+) also demonstrates good performance in terms of memory consumption since it use only a litter more memory than FP-growth* and less memory than PrePost and FIN. (C) 2015 Elsevier Ltd. All rights reserved.
This paper introduces a novel technique for optimal distribution system (dS) planning with distributed generation (dG) systems. It is being done to see how active and reactive power injections affect the system's ...
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This paper introduces a novel technique for optimal distribution system (dS) planning with distributed generation (dG) systems. It is being done to see how active and reactive power injections affect the system's voltage profile and energy losses. dG penetration in the power systems is one approach that has several advantages such as peak savings, loss lessening, voltage profile amelioration. It also intends to increase system reliability, stability, and security. The main goal of optimal distributed generation (OdG) is a guarantee to achieve the benefits mentioned previously to increase the overall system efficiency. For extremely vast and complicated systems, analytical approaches are not suitable and insufficient. Therefore, several meta-heuristic techniques are favored to obtain better performance from were convergence and accuracy for large systems. In this paper, an Improved Wild Horse Optimizationd* algorithm (IWHO) is proposed as a novel metaheuristic method for solving optimization issues in electrical power systems. IWHO is devised with inspirations from the social life behavior of wild horses. The suggested method is based on the horse's decency. To assess the efficacy of the IWHO, it is implemented on the 23 benchmark functions Reliability amelioration is the most things superb as a result of dGs incorporation. Thus, in this research, a customer-side reliability appraisal in the dS that having a dG unit was carried out by a Monte Carlo Simulation (MCS) approach to construct an artificial history for each ingredient across simulation duration. For load flow calculations, the backward Forward Sweep (BFS) technique has been employed as a simulation tool to assess the network performance considering the power handling restrictions. The proposed IWHO method has been measured on IEEE 33 69 and 119 buses to ascertain the network performing in the presence of the optimal dG and the potential benefits of the suggested technique for enhancing the tools used by opera
We discuss and evaluate the current state of second-order and higher-order multivariate calibration methods devoted to the determination of compounds in non-multilinear data systems. We examine possible causes of mult...
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We discuss and evaluate the current state of second-order and higher-order multivariate calibration methods devoted to the determination of compounds in non-multilinear data systems. We examine possible causes of multilinearity deviations: (1) a non-linear relationship between signal and analyte concentration;(2) a signal for a given sample that is non-multilinear;and, (3) component profiles that are not constant across the different samples. We discuss the advantages and the limitations of thed* algorithms available to cope with these different situations. The review covers relevant analytical problems found in samples of environmental and biological interest, highlighting some significant examples, and evaluating the advantages and the limitations of the differentd* algorithms available. (C) 2011 Elsevier Ltd. All rights reserved.
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