Biological Weighting Function of the UV-B–induced Impairment of Phototaxis in the Freshwater Ciliate Ophryoglena flava¶

The low-frequency noise generated by the vibration of the compressor in the machinery room of refrigerators is considered as annoying sound. Active noise control is used to reduce this noise without any change in the design of the compressor in the machinery room. In configuring the control system, various signals are measured and analyzed to select the reference signal that best represents the compressor noise. As the space inside the machinery room is small, the size of a speaker is limited, and the magnitude of the controller transfer function is designed to be small at low frequencies, the controller uses FIR filter structure converged by the FxLMS algorithm using the pre-measured time signal. To manage the convergence speed for each frequency, the frequency-weighting function is applied to FxLMS algorithm. A series of measurements are performed to design the controller and to evaluate the control performance. After the control, the sound power transmitted by the refrigerator is reduced by 9 dB at the first dominant frequency (408 Hz in this case) and 3 dB at the second dominant frequency (459 Hz here), and the overall sound power decreases by 2.6 dB. Through this study, an active control system for the noise generated by refrigerator compressors is established.

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Towards Robust Multiple Blind Source Localization Using Source Separation and Beamforming

Sensors ◽

10.3390/s21020532 ◽

2021 ◽

Vol 21 (2) ◽

pp. 532

Author(s):

Henglin Pu ◽

Chao Cai ◽

Menglan Hu ◽

Tianping Deng ◽

Rong Zheng ◽

...

Keyword(s):

Source Localization ◽

Sound Source ◽

Indoor Localization ◽

Weighting Function ◽

Signal To Noise Ratio ◽

Source Separation ◽

Sound Source Localization ◽

Angle Of Arrival ◽

Sound Sources ◽

Localization Algorithms

Multiple blind sound source localization is the key technology for a myriad of applications such as robotic navigation and indoor localization. However, existing solutions can only locate a few sound sources simultaneously due to the limitation imposed by the number of microphones in an array. To this end, this paper proposes a novel multiple blind sound source localization algorithms using Source seParation and BeamForming (SPBF). Our algorithm overcomes the limitations of existing solutions and can locate more blind sources than the number of microphones in an array. Specifically, we propose a novel microphone layout, enabling salient multiple source separation while still preserving their arrival time information. After then, we perform source localization via beamforming using each demixed source. Such a design allows minimizing mutual interference from different sound sources, thereby enabling finer AoA estimation. To further enhance localization performance, we design a new spectral weighting function that can enhance the signal-to-noise-ratio, allowing a relatively narrow beam and thus finer angle of arrival estimation. Simulation experiments under typical indoor situations demonstrate a maximum of only 4∘ even under up to 14 sources.

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Enhanced context-aware recommendation using topic modeling and particle swarm optimization

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210331 ◽

2021 ◽

pp. 1-16

Author(s):

Ibtissem Gasmi ◽

Mohamed Walid Azizi ◽

Hassina Seridi-Bouchelaghem ◽

Nabiha Azizi ◽

Samir Brahim Belhaouari

Keyword(s):

Topic Modeling ◽

Latent Dirichlet Allocation ◽

State Of The Art ◽

Weighting Function ◽

Contextual Factors ◽

Pearson Correlation ◽

Correlation Coefficients ◽

Pso Algorithm ◽

Context Aware ◽

Proposed Model

Context-Aware Recommender System (CARS) suggests more relevant services by adapting them to the user’s specific context situation. Nevertheless, the use of many contextual factors can increase data sparsity while few context parameters fail to introduce the contextual effects in recommendations. Moreover, several CARSs are based on similarity algorithms, such as cosine and Pearson correlation coefficients. These methods are not very effective in the sparse datasets. This paper presents a context-aware model to integrate contextual factors into prediction process when there are insufficient co-rated items. The proposed algorithm uses Latent Dirichlet Allocation (LDA) to learn the latent interests of users from the textual descriptions of items. Then, it integrates both the explicit contextual factors and their degree of importance in the prediction process by introducing a weighting function. Indeed, the PSO algorithm is employed to learn and optimize weights of these features. The results on the Movielens 1 M dataset show that the proposed model can achieve an F-measure of 45.51% with precision as 68.64%. Furthermore, the enhancement in MAE and RMSE can respectively reach 41.63% and 39.69% compared with the state-of-the-art techniques.

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A Modified 2-DOF Control Framework and GA Based Intelligent Tuning of PID Controllers

Processes ◽

10.3390/pr9030423 ◽

2021 ◽

Vol 9 (3) ◽

pp. 423

Author(s):

Gun-Baek So

Keyword(s):

Disturbance Rejection ◽

Pid Controller ◽

Weighting Function ◽

Pid Controllers ◽

Set Point ◽

Tuning Method ◽

Point Tracking ◽

Load Disturbance ◽

Control Framework ◽

Feedforward Controller

Although a controller is well-tuned for set-point tracking, it shows poor control results for load disturbance rejection and vice versa. In this paper, a modified two-degree-of-freedom (2-DOF) control framework to solve this problem is proposed, and an optimal tuning method for the pa-rameters of each proportional integral derivative (PID) controller is discussed. The unique feature of the proposed scheme is that a feedforward controller is embedded in the parallel control structure to improve set-point tracking performance. This feedforward controller and the standard PID con-troller are combined to create a new set-point weighted PID controller with a set-point weighting function. Therefore, in this study, two controllers are used: a set-point weighted PID controller for set-point tracking and a conventional PID controller for load disturbance rejection. The parameters included in the two controllers are tuned separately to improve set-point tracking and load dis-turbance rejection performances, respectively. Each controller is optimally tuned by genetic algo-rithm (GA) in terms of minimizing the IAE performance index, and what is special at this time is that it also tunes the set-point weighting parameter simultaneously. The simulation results performed on four virtual processes verify that the proposed method shows better performance in set-point tracking and load disturbance rejection than those of the other methods.

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k-Nearest Neighbor Learning with Graph Neural Networks

Mathematics ◽

10.3390/math9080830 ◽

2021 ◽

Vol 9 (8) ◽

pp. 830

Author(s):

Seokho Kang

Keyword(s):

Neural Network ◽

Nearest Neighbor ◽

Learning Algorithm ◽

Weighting Function ◽

High Sensitivity ◽

Training Data ◽

K Nearest Neighbor ◽

Main Challenge ◽

Benchmark Datasets ◽

Graph Neural Networks

k-nearest neighbor (kNN) is a widely used learning algorithm for supervised learning tasks. In practice, the main challenge when using kNN is its high sensitivity to its hyperparameter setting, including the number of nearest neighbors k, the distance function, and the weighting function. To improve the robustness to hyperparameters, this study presents a novel kNN learning method based on a graph neural network, named kNNGNN. Given training data, the method learns a task-specific kNN rule in an end-to-end fashion by means of a graph neural network that takes the kNN graph of an instance to predict the label of the instance. The distance and weighting functions are implicitly embedded within the graph neural network. For a query instance, the prediction is obtained by performing a kNN search from the training data to create a kNN graph and passing it through the graph neural network. The effectiveness of the proposed method is demonstrated using various benchmark datasets for classification and regression tasks.

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Method for Measuring the Second-Order Moment of Atmospheric Turbulence

Atmosphere ◽

10.3390/atmos12050564 ◽

2021 ◽

Vol 12 (5) ◽

pp. 564

Author(s):

Hong Shen ◽

Longkun Yu ◽

Xu Jing ◽

Fengfu Tan

Keyword(s):

Atmospheric Turbulence ◽

Weighting Function ◽

Structure Constant ◽

Turbulence Models ◽

Second Order ◽

Index Structure ◽

Order Moment ◽

Site Testing ◽

Optical Effects ◽

Novel Method

The turbulence moment of order m (μm) is defined as the refractive index structure constant Cn2 integrated over the whole path z with path-weighting function zm. Optical effects of atmospheric turbulence are directly related to turbulence moments. To evaluate the optical effects of atmospheric turbulence, it is necessary to measure the turbulence moment. It is well known that zero-order moments of turbulence (μ0) and five-thirds-order moments of turbulence (μ5/3), which correspond to the seeing and the isoplanatic angles, respectively, have been monitored as routine parameters in astronomical site testing. However, the direct measurement of second-order moments of turbulence (μ2) of the whole layer atmosphere has not been reported. Using a star as the light source, it has been found that μ2 can be measured through the covariance of the irradiance in two receiver apertures with suitable aperture size and aperture separation. Numerical results show that the theoretical error of this novel method is negligible in all the typical turbulence models. This method enabled us to monitor μ2 as a routine parameter in astronomical site testing, which is helpful to understand the characteristics of atmospheric turbulence better combined with μ0 and μ5/3.

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Conservative finite-volume forms of the Saint-Venant equations for hydrology and urban drainage

Hydrology and Earth System Sciences ◽

10.5194/hess-23-1281-2019 ◽

2019 ◽

Vol 23 (3) ◽

pp. 1281-1304 ◽

Cited By ~ 8

Author(s):

Ben R. Hodges

Keyword(s):

Free Surface ◽

Finite Volume ◽

Source Term ◽

Weighting Function ◽

Bottom Topography ◽

Urban Drainage ◽

Venant Equation ◽

Saint Venant Equations ◽

Volume Forms ◽

Conservative Flux

Abstract. New integral, finite-volume forms of the Saint-Venant equations for one-dimensional (1-D) open-channel flow are derived. The new equations are in the flux-gradient conservation form and transfer portions of both the hydrostatic pressure force and the gravitational force from the source term to the conservative flux term. This approach prevents irregular channel topography from creating an inherently non-smooth source term for momentum. The derivation introduces an analytical approximation of the free surface across a finite-volume element (e.g., linear, parabolic) with a weighting function for quadrature with bottom topography. This new free-surface/topography approach provides a single term that approximates the integrated piezometric pressure over a control volume that can be split between the source and the conservative flux terms without introducing new variables within the discretization. The resulting conservative finite-volume equations are written entirely in terms of flow rates, cross-sectional areas, and water surface elevations – without using the bottom slope (S0). The new Saint-Venant equation form is (1) inherently conservative, as compared to non-conservative finite-difference forms, and (2) inherently well-balanced for irregular topography, as compared to conservative finite-volume forms using the Cunge–Liggett approach that rely on two integrations of topography. It is likely that this new equation form will be more tractable for large-scale simulations of river networks and urban drainage systems with highly variable topography as it ensures the inhomogeneous source term of the momentum conservation equation is Lipschitz smooth as long as the solution variables are smooth.

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