A Quantifying Approach to Soil Salinity Based on a Radar Feature Space Model Using ALOS PALSAR-2 Data

In arid and semi-arid areas, timely and effective monitoring and mapping of salt-affected areas is essential to prevent land degradation and to achieve sustainable soil management. The main objective of this study is to make full use of synthetic aperture radar (SAR) polarization technology to improve soil salinity mapping in the Keriya Oasis, Xinjiang, China. In this study, 25 polarization features are extracted from ALOS PALSAR-2 images, of which four features are selected. In addition, three soil salinity inversion models, named the RSDI1, RSDI2, and RSDI3, are proposed. The analysis and comparison results of inversion accuracy show that the overall correlation values of the RSDI1, RSDI2, and RSDI3 models are 0.63, 0.61, and 0.62, respectively. This result indicates that the radar feature space models have the potential to extract information on soil salinization in the Keriya Oasis.

Variational Bayesian-Based Improved Maximum Mixture Correntropy Kalman Filter for Non-Gaussian Noise

The maximum correntropy Kalman filter (MCKF) is an effective algorithm that was proposed to solve the non-Gaussian filtering problem for linear systems. Compared with the original Kalman filter (KF), the MCKF is a sub-optimal filter with Gaussian correntropy objective function, which has been demonstrated to have excellent robustness to non-Gaussian noise. However, the performance of MCKF is affected by its kernel bandwidth parameter, and a constant kernel bandwidth may lead to severe accuracy degradation in non-stationary noises. In order to solve this problem, the mixture correntropy method is further explored in this work, and an improved maximum mixture correntropy KF (IMMCKF) is proposed. By derivation, the random variables that obey Beta-Bernoulli distribution are taken as intermediate parameters, and a new hierarchical Gaussian state-space model was established. Finally, the unknown mixing probability and state estimation vector at each moment are inferred via a variational Bayesian approach, which provides an effective solution to improve the applicability of MCKFs in non-stationary noises. Performance evaluations demonstrate that the proposed filter significantly improves the existing MCKFs in non-stationary noises.

Estimating Distributions of Parameters in Nonlinear State Space Models with Replica Exchange Particle Marginal Metropolis–Hastings Method

Extracting latent nonlinear dynamics from observed time-series data is important for understanding a dynamic system against the background of the observed data. A state space model is a probabilistic graphical model for time-series data, which describes the probabilistic dependence between latent variables at subsequent times and between latent variables and observations. Since, in many situations, the values of the parameters in the state space model are unknown, estimating the parameters from observations is an important task. The particle marginal Metropolis–Hastings (PMMH) method is a method for estimating the marginal posterior distribution of parameters obtained by marginalization over the distribution of latent variables in the state space model. Although, in principle, we can estimate the marginal posterior distribution of parameters by iterating this method infinitely, the estimated result depends on the initial values for a finite number of times in practice. In this paper, we propose a replica exchange particle marginal Metropolis–Hastings (REPMMH) method as a method to improve this problem by combining the PMMH method with the replica exchange method. By using the proposed method, we simultaneously realize a global search at a high temperature and a local fine search at a low temperature. We evaluate the proposed method using simulated data obtained from the Izhikevich neuron model and Lévy-driven stochastic volatility model, and we show that the proposed REPMMH method improves the problem of the initial value dependence in the PMMH method, and realizes efficient sampling of parameters in the state space models compared with existing methods.

Klasifikasi Berita Hoaks Menggunakan Algoritma Vector Space Model

Sebuah berita terkait suatu informasi yang beredar di media cetak atau mainstream akan menjadikan opini publik tentang suatu masalah baik yang bersifat informasi positif atau negatif, perkembangan teknologi informasi sekarang ini menyebabkan penyebaran informasi bisa uptodate setiap harinya. Dengan semakin mudahnya sebuah informasi menyebar maka akan semakin mudah pula mempengaruhi kehidupan dalam sosial masyarakat sekarang ini. Namun pada kenyataannya informasi yang beredar di media itu tidak semuanya benar atau bisa dikatakan adanya suatu berita hoax atau tidak benar. Dalam penelitian ini bertujuan untuk mengklasifikasi sistem temu kembali informasi berita hoaks menggunakan metode vektor space model untuk memastikan kebenaran suatu berita apakah berita hoax atau tidak. Dalam penelitian tersebut menghasilkan klasifikasi kebenaran berita dengan akurasi terbaik pada K-6 sebesar 83%, artinya dengan akurasi tersebut bisa memvalidasi klasifikasi terkait informasi berita benar ataupun hoax sebesar 83%.

Color changes of ash wood (Fraxinus excelsior L.) caused by thermal modification in air and steam

Color changes of ash wood (Fraxinus excelsior L.) caused by thermal modification in air and steam. Ash wood samples of 20x20x30 mm were subjected to thermal modification in different conditions. The thermal modification was conducted in air at 190 °C and in steam at 160 °C. For both environments modification lasted 2, 6 and 10 hours. Samples color parameters were measured before and after thermal modification on the basis of the mathematical CIELab color space model. Changes in all parameters (L, a and b) were observed, the highest in lightness (L) - darker color. The total color difference (ΔE) and chromaticity change (ΔC) were calculated for all samples. The highest value of ΔE was obtained for wood modified in the air at 190 °C for 10 h. The highest value of ΔC was obtained for wood modified in steam at 160 °C for 10 h. However, the value obtained for wood modified in the air at 190 °C for 10 h were only slightly lower.

Installation for studying the parameters of localiInstallation for studying the parameters of localization of an electromagnetic wave in a waveguide of variable cross-section in the framework of the predictions of the 5-D extended space modelzation of an electromagnetic wave in a waveguide of variable cross-section in the framework of the predictions of the 5-D extended space model

The paper describes the creation and testing of an experimental setup for studying the parameters of localization of electromagnetic microwave radiation with a power of 0.001-0.004 W in the range of 36.0-79.0 GHz when propagating radiation in metal waveguides of variable cross-section. Measurements will also be carried out under conditions of filling the waveguide with dielectric materials with refractive indices from 1.46 to 4.0 for microwave radiation of the specified range. The installation is designed to measure the parameters of the localization of microwave radiation when it passes through a waveguide of variable cross-section, filled with materials with different refractive indices. Interpretation of the results will be carried out within the framework of the 5-D extended space model (ESM). The extended space model is formulated in (1+4)-dimensional space time-coordinate-interval. An additional spatial coordinate in the ESM is the interval. In the conjugate 5-D space, the energy-momentum-mass interval in the ESM corresponds to mass. In the ESM formalism, the question of the appearance of a nonzero variable mass in a photon and its localization under the influence of an external field is studied.