Combining flamelet-generated manifold and machine learning models in simulation of a non-premixed diffusion flame

Flamelet Generated Manifold (FGM) is an example of a chemistry tabulation or a flamelet method that is under attention because of its accuracy and speed in predicting combustion characteristics. However, the main problem in applying the model is a large amount of memory required. One way to solve this problem is to apply machine learning (ML) to replace the stored tabulated data. Four different machine learning methods, including two Artificial Neural Networks (ANNs), a Random Forest (RF), and a Gradient Boosted Trees (GBT), are trained, validated, and compared in terms of various performance measures. The progress variable source term and transport properties are replaced with the ML models. Particular attention was paid to the progress variable source term due to its high gradient and wide range of its value in the control variables space. Data preprocessing is shown to play an essential role in improving the performance of the models. Two ensemble models, namely RF and GBT, exhibit high training efficiency and acceptable accuracy. On the other hand, the ANN models have lower training errors and take longer to train. The four models are then combined with a one-dimensional combustion code to simulate a counterflow non-premixed diffusion flame in engine-relevant conditions. The predictions of the ML-FGM models are compared with detailed chemical simulations and the original FGM model for key combustion properties and representative species profiles

Privacy-preserving reversible information hiding based on arithmetic of quadratic residues

The phenomenal advances of cloud computing technology have given rise to the research area of privacy-preserving signal processing, which aims to preserve information privacy even when the signals are processed in an insecure environment. Privacy-preserving information hiding is a multidisciplinary study that has opened up a great deal of intriguing real-life applications, such as data exfiltration prevention, data origin authentication, and electronic data management. Information hiding is a practice of embedding intended messages into carrier signals through imperceptible alterations. In view of some content-sensitive scenarios, however, the ability to preserve perfect copies of signals is of crucial importance, for instance, considering the inadequate robustness of recent artificial intelligence-aided automated systems against noise perturbations. Reversibility of information hiding systems is a valuable property that permits recovery of original carrier signals if desired. In this paper, we propose a novel privacy-preserving reversible information hiding scheme inspired by the mathematical concept of quadratic residues. A quadratic residue has four (not necessarily distinct) square roots, which enables payloads to be encoded in a dynamic fashion. Furthermore, a predictive model based upon the projection theorem is devised to assist carrier signal recovery. The experimental results showed significant improvements over the state-of-the-art methods with regard to capacity, fidelity, and reversibility

Visual Detection and Association Tracking of Dim Small Ship Targets from Optical Image Sequences of Geostationary Satellite Using Multispectral Radiation Characteristics

By virtue of the merits of wide swath, persistent observation, and rapid operational response, geostationary remote sensing satellites (e.g., GF-4) show tremendous potential for sea target system surveillance and situational awareness. However, ships in such images appear as dim small targets and may be affected by clutter, reef islands, clouds, and other interferences, which makes the task of ship detection and tracking intractable. Considering the differences in visual saliency characteristics across multispectral bands between ships and jamming targets, a novel approach to visual detecting and association tracking of dense ships based on the GF-4 image sequences is proposed in this paper. First, candidate ship blobs are segmented in each single-spectral image of each frame through a multi-vision salient features fusion strategy, to obtain the centroid position, size, and corresponding spectral grayscale information of suspected ships. Due to the displacement of moving ships across multispectral images of each frame, multispectral association with regard to the positions of ship blobs is then performed to determine the final ship detections. Afterwards, precise position correction of detected ships is implemented for each frame in image sequences via multimodal data association between GF-4 detections and automatic identification system data. Last, an improved multiple hypotheses tracking algorithm with multispectral radiation and size characteristics is put forward to track ships across multi-frame corrected detections and estimate ships’ motion states. Experiment results demonstrate that our method can effectively detect and track ships in GF-4 remote sensing image sequences with high precision and recall rate, yielding state-of-the-art performance

The Impact of Development Zones on China’s Urbanization from the Perspectives of the Population, Land, and the Economy

The sustainable development of urbanization is a necessary condition for China to realize modernization. Considering the importance of urbanization to China’s future development and the advantages of development zones in promoting urbanization, it is necessary to quantify the impact of establishing development zones on urbanization development. Using the difference in difference (DID) model, this study takes the panel data of 235 cities in China from 1990 to 2017 to evaluate the policy effects of setting up development zones on urbanization from the perspectives of the population, land, and the economy. The results show that the development zone policy in the overall panel exerts a significant negative impact on land urbanization and a significant positive impact on economic urbanization but exerts no significant impact on population urbanization. The regression results of sub-regions show significant regional differences in the impact of development zones on urbanization. In the eastern region, the development zone policy has promoted the intensive use of urban construction land. For the central and western regions with weak development foundations, development zones play an important role in attracting the population and upgrading industries while reducing the intensive use of construction land. This study provides urban-level empirical evidence for evaluating the urbanization effects of development zone policies and puts forward policy recommendations for development zone construction to promote high-quality urbanization in China

Algal Decay Resistance of Conventional and Novel Wood-Based Composites

Measures of the resistance to algal decay of conventional (medium density fiberboard [MDF] and plywood) and novel wood-based composites (WPC) were investigated in the same or varying wood species by using an artificial accelerated test with four mixed algal suspensions (Chlorella vulgaris, Ulothrix sp., Scenedesmus quadricauda, and Oscillatoria sp.). The morphology characterization of the surface and fracture of the specimens was analyzed using scanning electron microscopy (SEM) and a digital instrument. The pH value and the mass loss rate of the different wood species were also tested. The results showed that the algal resistance of the MDF and plywood were superior to that of the WPC of the same wood species. Furthermore, the algal resistance capacity of WPC made from various wood species were ranked as: Liquidambar formosana > Cunninghamia lanceolata and Melaleuca leucadendra > Ricinus communis > Eucalyptus grandis × E. urophylla and Pinus massoniana. There was a close relationship between the pH value and the algal resistance level; as the pH value increased, the alga resistance of the WPC also increased. The algal colonization only had a negative effect on the appearance of the samples

Study on the Torque Rheological Behavior of Wood Flour/Chitosan/Polyvinyl Chloride Composites

Torque rheological properties of wood flour/chitosan/PVC (WF/CS/PVC) compounds were measured by a torque rheometer using roller-style rotating blades at various setting temperatures (175 and 185 °C) and rotation speeds (30, 45, 60, and 75 rpm). The torque rheological parameters were calculated based on the Marquez model and Arrhenius equation. The torque rheological curves of WF/CS/PVC composites were similar to WF/PVC composites without chitosan. The classical Marquez model was verified to be suitable for both WF/PVC and WF/CS/PVC composites. Specifically, the activation energy (ΔE), n value, and range of C(n)m for the former and latter were 27.698 kJ•mol-1 and 29.237 kJ•mol-1, 0.382 and 0.381, and 4.415 to 5.749 N•m•sn and 4.652 to 6.079 N•m•sn, respectively. The rheological properties of WF/CS/PVC composites did not show a great qualitative enhancement compared to WF/PVC composites

Effects of Natural Chitosan as Biopolymer Coupling Agent on the Pyrolysis Kinetics of Wood Flour/ Polyvinyl Chloride Composites

The thermal degradation behavior and pyrolysis kinetic models of wood flour (WF)/PVC composites before and after adding chitosan were studied using thermogravimetry (TGA) and nine common kinetic model functions (f(α)). The results indicated that the thermal degradation temperature of WF/PVC composites was delayed to a higher value after adding chitosan. The first-order reaction order (L1) model and second-order reaction order (L2) model were found to be the best reaction order functions for the description of mass loss kinetics of WF/PVC without chitosan during the first and second stages. Two L2 models were suitable for both degradation stages of WF/CS/PVC. Activation energy (E) and frequency factor (A) for WF/PVC and WF/CS/PVC corresponded to 26.05 kJ·mol-1, 4.08×103 s-1, and 40.89 kJ·mol-1, 2.11×1010 s-1 at the first degradation stage, respectively, and 97.83 kJ·mol-1, 1.11×107 s-1 and 92.88 kJ·mol-1, 1.56×1011 s-1 at the second degradation stage

Surface Roughness Prediction of Titanium Alloy during Abrasive Belt Grinding Based on an Improved Radial Basis Function (RBF) Neural Network

Titanium alloys have become an indispensable material for all walks of life because of their excellent strength and corrosion resistance. However, grinding titanium alloy is exceedingly challenging due to its pronounced material characteristics. Therefore, it is crucial to create a theoretical roughness prediction model, serving to modify the machining parameters in real time. To forecast the surface roughness of titanium alloy grinding, an improved radial basis function neural network model based on particle swarm optimization combined with the grey wolf optimization method (GWO-PSO-RBF) was developed in this study. The results demonstrate that the improved neural network developed in this research outperforms the classical models in terms of all prediction parameters, with a model-fitting R2 value of 0.919