Influence of Clay Fraction and Mineral Composition on Unsaturated Characteristics of Cohesive Foundation Soil in Airports

Taking unsaturated clay foundation soil of an airport project in Hefei as the research object, the effects of particle gradation and mineral composition on the unsaturated soil properties were analyzed through two kinds of tests. The results show that there is a good correlation between the residual water content and the clay fraction or silt fraction content in the grading, and the residual water content has a significant positive linear correlation with the clay fraction content, but a negative linear correlation with the silt fraction content. Residual matric suction has a nonlinear correlation with clay fraction or silt fraction content in gradation, which has a significant nonlinear negative correlation with clay fraction content and a positive nonlinear correlation with silt fraction content. The residual water content and the residual matric suction have obvious linear relationship with the content of montmorillonite but have no obvious correlation with the content of illite. The water-storage coefficient of unsaturated airfield foundation soil decreases exponentially with the increase of clay content and montmorillonite content.

SOH estimation method for lithium-ion batteries based on nonlinear autoregressive neural network model with exogenous input

The working state of lithium-ion batteries must be estimated accurately and efficiently in the battery management system. Building a model is the most prevalent way of predicting the battery's working state. Based on the variable order equivalent circuit model, this paper examines the attenuation curve of battery capacity with the number of cycles. It identifies the order of the equivalent circuit model using Bayesian Information Criterion (BIC). Based on the correlation between capacity and resistance, the paper concludes that there is a nonlinear correlation between model parameters and state of health (SOH). The nonlinear autoregressive neural network with exogenous input (NARX) is used to fit the nonlinear correlation for capacity regeneration. Then, the self-adaptive weight particle swarm optimization (SWPSO) method is suggested to train the neural network. Finally, single-battery and multi-battery tests are planned to validate the accuracy of the SWPSO-NARX estimate of SOH. The experimental findings indicate that the SOH estimate effect is significant.

A Novel 4D Track Prediction Approach Combining Empirical Mode Decomposition with Nonlinear Correlation Coefficient

The accuracy of 4D track prediction plays an important role to solve the prominent contradiction between the rapid development of air transport industry and the limited resources of airspace. The conventional 4D track prediction based on the aerospace dynamic model is usually inaccurate since of weather influence and air traffic controller (ATC) factor. In this paper, an entirely data-driven nominal flight height profile prediction approach combing empirical mode decomposition (EMD) with nonlinear correlation coefficient (NCC) is proposed. Firstly, the historical tracks are implemented on EMD individually. Then according to a procedure similar to leave-one-out cross validation (LOOCV), the physical meanings of different intrinsic mode functions (IMFs) obtained by EMD are analyzed to corresponding to the various flight information. For a specified flight, the similarities between different dates are measured by NCC. Finally, a predicted nominal trajectory is obtained by summing a series of selected IMFs with a regression weight under least square optimization framework. It is demonstrated that the proposed method shows a higher prediction performance when comparing with the state of the art method named as nearest neighbor classification with dynamic time warping (DTW). La precisión de la predicción de la pista 4D desempeña un papel importante para resolver la importante contradicción entre el rápido desarrollo de la industria del transporte aéreo y los recursos limitados del espacio aéreo. La predicción convencional de la pista 4D basada en el modelo dinámico aeroespacial suele ser inexacta debido a la influencia de las condiciones meteorológicas y el factor del controlador de tráfico aéreo (ATC). En este trabajo, se propone un enfoque de predicción del perfil de altura de vuelo nominal totalmente basado en datos que combina la descomposición empírica de modos (EMD) con el coeficiente de correlación no lineal (NCC). En primer lugar, las pistas históricas se implementan en la EMD individualmente. A continuación, de acuerdo con un procedimiento similar al de la validación cruzada sin intervención (LOOCV), se analizan los significados físicos de las diferentes funciones de modo intrínseco (IMF) obtenidas por la EMD para que correspondan a las diversas informaciones de vuelo. Para un vuelo específico, se miden las similitudes entre las distintas fechas mediante NCC. Por último, se obtiene una trayectoria nominal predicha mediante la suma de una serie de FMI seleccionadas con un peso de regresión en el marco de la optimización de mínimos cuadrados. Se demuestra que el método propuesto muestra un mayor rendimiento de predicción en comparación con el método más avanzado denominado clasificación de vecinos más cercanos con deformación temporal dinámica (DTW).

Dimensional characteristics of grist intermediate products obtained in the first two technological passages in the reduction phase of an industrial milling plant

After the wheat is coarsely grinded in the breakage technological phase and after a certain percentage of flour and bran has been extracted here, the crushing is continued in the reduction technological phase. The paper presents the flow of grist products at the first two technological passages from the reduction phase of an industrial milling unit. Samples taken from these two technological passages were subjected to a granulometric analysis, and with the experimental data a nonlinear correlation was performed with the Rosin-Rammler law, obtaining correlation coefficients of over 0.954. The paper also discusses the limits of the dimensions between which the particles of each fraction are sorted at the first two technological passages in the reduction phase. The analysis performed in this paper can serve in establishing the fabrics of the sifting frames from the plansifter compartments, respectively when adjusting the roller mills.

Nonlinear Correlation Analysis of Time Series Based on Complex Network Similarity

Characterizing the relationship between time series is an important issue in many fields, in particular, in many cases there is a nonlinear correlation between series. This paper provides a new method to study the relationship between time series using the perspective of complex networks. This method converts a time series into a distance matrix and constructs a sequence of nearest neighbor networks, so that the nonlinear relationship between time series is expressed as similarity between networks. In addition, based on the surrogate series, we applied [Formula: see text]-score to characterize the level of significance and analyzed some benchmark models. We not only use the artificial dataset and the real dataset to verify the effectiveness of the proposed method, but also analyze its robustness, which provides an alternative method for detecting nonlinear relationships.