Research on Segmentation Experience of Music Signal Improved Based on Maximization of Negative Entropy

With the rapid growth of digital music today, due to the complexity of the music itself, the ambiguity of the definition of music category, and the limited understanding of the characteristics of human auditory perception, the research on topics related to automatic segmentation of music is still in its infancy, while automatic music is still in its infancy. Segmentation is a prerequisite for fast and effective retrieval of music resources, and its potential application needs are huge. Therefore, topics related to automatic music segmentation have important research value. This paper studies an improved algorithm based on negative entropy maximization for well-posed speech and music separation. Aiming at the problem that the separation performance of the negative entropy maximization method depends on the selection of the initial matrix, the Newton downhill method is used instead of the Newton iteration method as the optimization algorithm to find the optimal matrix. By changing the descending factor, the objective function shows a downward trend, and the dependence of the algorithm on the initial value is reduced. The simulation experimental results show that the algorithm can separate the source signal well under different initial values. The average iteration time of the improved algorithm is reduced by 26.2%, the number of iterations is reduced by 69.4%, and the iteration time and the number of iterations are both small. Fluctuations within the range better solve the problem of sensitivity to the initial value. Experiments have proved that the new objective function can significantly improve the separation performance of neural networks. Compared with the existing music separation methods, the method in this paper shows excellent performance in both accompaniment and singing in separated music.

Influence of partitioning methods on computational cost of cfd simulations applied to hydrocyclones

In simulations, when using multipartitioned computers, the way in which the mesh is partitioned directly affects the average time per iteration, and therefore, the computational cost. This study proposes an analysis of the average time per iteration of 23 different partition methods available for tridimensional mesh in software FLUENT 19.2. For the calculation of the average iteration time, 100 iterations were used. Generally, the best partitioning methods were those in which the mesh division was made perpendicularly to the axis of the equipment. It was stated the choice of an adequate partitioning method can save high costs of computational power. For the hydrocyclone studied, with a computer with 8 cores, approximately 24.56 hours of simulation were saved, representing almost 20% of the total time.

An Iteration-Time Combination Method to Reduce the Dynamic Degradation of Digital Chaotic Maps

A new and improved method based on the number of iterations is proposed to reduce the dynamical degradation of the digital chaotic map in this study. We construct a control function by introducing iteration time instead of external systems, thereby replacing the control parameters in the original chaotic map. Experimental results show that the chaotic map based on the iteration-time combination method is more complicated and effective. The period is extended without completely destroying the phase space, which indicates that our method is effective and can compete with other proposed techniques. A type of pseudorandom bit generator based on the iteration-time combination method is proposed to demonstrate its simple application.

A Novel Learning Rate Schedule in Optimization for Neural Networks and It’s Convergence

The process of machine learning is to find parameters that minimize the cost function constructed by learning the data. This is called optimization and the parameters at that time are called the optimal parameters in neural networks. In the process of finding the optimization, there were attempts to solve the symmetric optimization or initialize the parameters symmetrically. Furthermore, in order to obtain the optimal parameters, the existing methods have used methods in which the learning rate is decreased over the iteration time or is changed according to a certain ratio. These methods are a monotonically decreasing method at a constant rate according to the iteration time. Our idea is to make the learning rate changeable unlike the monotonically decreasing method. We introduce a method to find the optimal parameters which adaptively changes the learning rate according to the value of the cost function. Therefore, when the cost function is optimized, the learning is complete and the optimal parameters are obtained. This paper proves that the method ensures convergence to the optimal parameters. This means that our method achieves a minimum of the cost function (or effective learning). Numerical experiments demonstrate that learning is good effective when using the proposed learning rate schedule in various situations.

Kalium Nitrat (KNO3): Karakteristik Senyawa dan Transpor Ion

Kalium Nitrat merupakan garam anorganik dengan rumus kimia KNO3. Kalium Nitrat termasuk senyawa ionik yang disusun oleh kation K+ dan NO3- dan merupakan sumber nitrogen paling penting dialam. Kalium nitrat bersifat polar yang dapat larut di dalam air, 133 g/L (00C) dan 316 g/L (200C). Penelitian yang dilakukukan terhadap Kalium Nitrat digunakan untuk menganalisis struktur molekul dan sifat-sifat transpor ion senyawa menggunakan kalkulasi matematis dan pemodelan dari ChemOffice 15.0. Data lain seperti sifat-sifat atom penyusun dan sifat transpor ion didapat dengan review beeberapa jurnal dari beberapa sumber sesuai dengan bagan Fishbone yang telah dibuat. Mr KNO3: 101 g/mol, ρ: 2,109 g/cm3, titik leleh: 3340C dan titik didih: 4000C yang membuat KNO3 terdekomposisi menjadi KNO2. Mobilitas kation K+: 7,62 m2s-1V-1 dan NO3-: 7,40 m2s-1V-1. Konduktivitas KNO3: 0,09479 x 10-8 mho/cm. kecepatan hanyut K+: 7,62 m3s-1V-1 dan NO3- = 15,429 X 10−8 m/s. Larutan ini memiliki viskositas (𝜂) = 0,216 𝑋 10−3 𝑚𝑃𝑎.𝑠. Optimasi MM2 minimization menunjukkan bahwa Kalium Nitrat memiliki Stretch: 0.0518; Bend: 2.8626; Stretch-Bend: -0.0591; Torsion: 0.0000; Non-1,4 VDW: 3.5709; 1,4 VDW: 0.0000; Charge/Charge: -39.0035; Charge/Dipole: -3.8510; Dipole/Dipole: 0.0000; Total Energy: -36.4283 kcal/mol, pada kalkulasi MM2 Dynamics, Kalium Nitrat memiliki Iteration Time sebesar 3140.628; Total Energy -52.274; Potential Energy -55.683; Temperature 228.77. dan dengan kalkulasi MM2 Properties, Kalium Nitrat memiliki Stretch: 1.9202; Bend: 17.0060; Stretch-Bend: -2.7911; Torsion: 0.0000; Non-1,4 VDW: 12.3427; 1,4 VDW: 0.0000; Charge/Charge: -64.2742; Charge/Dipole: -19.4215; Dipole/Dipole: 0.0000; Total Energy: -55.2178 kcal/mol; The total energy of frame: -55.218 kcal/mol.

Foraging efficiency of a predator flock for randomly moving prey: A simulation study

Flocking behavior of animals is highly advantageous for taking food resources. The degree of the advantage is related to the ability of flock members to detect their prey and the mobility of prey individuals. In this study, to explore the relation, we constructed a model to simulate a predator flock and its randomly moving prey. The predator members have the prey detection ability, which was characterized as sensing distance, [Formula: see text], and a sensing angle, [Formula: see text]. The mobility of the prey individuals was characterized as the maximum traveling distance of an iteration time step, [Formula: see text]. The relative flock foraging efficiency, [Formula: see text], was defined as [Formula: see text]. [Formula: see text] and [Formula: see text] represent the spent time for the flock to eat all prey individuals and to uptake the last remaining 10% prey, respectively. Simulation results showed that [Formula: see text] increased, maximized, and decreased with the increase of [Formula: see text], regardless of [Formula: see text]. As the number of prey, [Formula: see text], increased, the tendency of the increasing and decreasing was diluted. The result was briefly discussed in relation to the flock foraging behavior and the development of the model toward applications for real ecosystems.