An improved norm-constrained set-membership normalized least mean square (INCSM-NLMS) algorithm is proposed for adaptive sparse channel estimation (ASCE). The proposed INCSM-NLMS algorithm is ...implemented by incorporating an lp-norm penalty into the cost function of the traditional set-membership normalized least mean square (SM-NLMS) algorithm, which is also denoted as lp-norm penalized SM-NLMS (LPSM-NLMS) algorithm. The derivation of the proposed LPSM-NLMS algorithm is given theoretically, resulting in a zero attractor in its iteration. By using this proposed zero attractor, the convergence speed is effectively accelerated and the channel estimation steady-state error is also observably reduced in comparison with the existing popular SM-NLMS algorithms for estimating exact sparse multipath channels. The estimation behaviors are investigated via a typical sparse wireless multipath channel, a typical network echo channel, and an acoustic channel. The computer simulation results show that the proposed LPSM-NLMS algorithm is better than those corresponding sparse SM-NLMS and traditional SM-NLMS algorithms when the channels are exactly sparse.
Magnetic fingerprint has a multitude of advantages in the application of indoor positioning, but as a weak magnetic field, the dynamic range of the data is limited, which exerts direct influence on ...the positioning accuracy. Aiming at resolving the problem wherein the indoor magnetic positioning results tremendously rest with the magnetic characteristics, this paper puts forward a method based on deep learning to fuse the temporal and spatial characteristics of magnetic fingerprints, to fully explore the magnetic characteristics and to obtain stable and trustworthy positioning results. First and foremost, the trajectory of the acquisition area is extracted by adopting the ameliorated random waypoint model, and the simulation of pedestrian trajectory is completed. Then, the magnetic sequence is obtained by mapping the magnetic data. Aside from that, considering the scale characteristics of the sequence, a scale transformation unit is designed to obtain multi-scale features. At length, the neural network self-attention mechanism is adopted to fuse multiple features and output the positioning results. By probing into the positioning results of dissimilar indoor scenes, this method can adapt to diverse scenes. The average positioning error in a corridor, open area and complex area reaches 0.65 m, 0.93 m and 1.38 m respectively. The addition of multi-scale features has certain reference value for ameliorating the positioning performance.
Unlike natural images, remote sensing scene images usually contain one scene label and many object labels, and many object labels are arranged dispersedly, which brings great difficulties to feature ...extraction of scene label. To accurately identify scene labels from remote sensing scene images with multiple object labels, it is important to fully understand the global context of the image. In order to solve the challenges of multi-label scene images and improve the classification performance, a global context feature extraction module is proposed in this paper. The module combines the semantics information of different regions through a global pooling and three different scale sub-regions pooling, which makes the module have stronger ability of global feature representation. In addition, in order to fully understand the semantic content of remote sensing images, a three branch joint feature extraction module is constructed, which consists of the global context feature module, 3 × 3 convolution branch and identity branch are fused. Finally, a lightweight convolution neural network based on joint features (LCNN-JF) is constructed using traditional convolution, depthwise separable convolution, joint feature extraction module and classifier for remote sensing scene image classification. A series of experimental results on four datasets, UCM, AID, RSSCN and NWPU, demonstrate that the proposed method has better feature representation ability and can achieve better classification of remote sensing scene images.
Novel scheelite‐type Ca0.55(Nd1‐xBix)0.3MoO4 (0.2 ≤ x ≤ 0.95) ceramics were prepared using the solid‐state reaction method. According to the X‐ray diffraction data, a solid solution was formed in ...0.2 ≤ x ≤ 0.95 and all the samples belong to pure scheelite phase with the tetragonal structure. As revealed by Raman spectroscopy, the number of vibrational modes decreased with the increase in x value, which further indicated that Bi3+ ions occupied A‐site of scheelite structure. As the x value increased, the sintering temperature decreased from 740°C to 660°C; the permittivity increased from 12.6 to 20.3; the Qf value first decreased slightly and gradually remained stable. Based on the infrared reflectivity spectrum analysis, the calculated permittivity derived from the fitted data shared the same trend with the measured value. The Ca0.55(Nd0.05Bi0.95)0.3MoO4 ceramic sintered at 660 °C attained a near‐zero value temperature coefficient ~τf (−7.1 ppm/°C) and showed excellent microwave dielectric properties with a ɛr ~ 20.3 and a Qf ~ 33 860 GHz, making this system a promising candidate in the ultralow temperature cofired ceramic (ULTCC) technology.
•The existing major heat conduction and radiation models are reviewed and evaluated.•For conduction, the Zehner-Bauer-Schlünder (ZBS) model is recommended in this work.•For radiation, Breitbach and ...Barthels (B-B) correlation can give acceptalbe results.•Temperature evolutions in four fixed-bed coal pyrolyzers are successfully predicted.
The existing major heat conduction and radiation models for packed bed of particles are reviewed and evaluated by comparing the predicted results with experimental data. For low-temperature condition, it is found that the Zehner-Bauer-Schlünder (ZBS) model is less sensitive to the effect of contact area and is thus recommended for the calculation of effective thermal conductivity of packed bed. For high-temperature condition, although numbers of models can be used to calculate the radiative heat transfer behavior in packed bed, the Breitbach and Barthels (B-B) correlation is the optimal method applicable for different particle diameters, emissivities and voidages. The results of CFD simulations using the identified optimal heat transfer models agree well with the thermal measurements by thermocouple in four coal pyrolysis fixed-bed reactors mounted with or without particularly designed internals.
The two westerly branches have a significant impact on the climate of the area on the eastern side of the Tibetan Plateau when flowing around it. A continuous drought event in Southwest China from ...the winter of 2009 to the spring of 2014 caused huge economic losses. This research focuses on the dynamic field anomalies over the Tibetan Plateau during this event using statistical analysis, attempts to decipher its mechanism on drought in Southwest China, and provides a regression model. We established that the anticyclone and downdraft over the Tibetan Plateau were weaker than usual during the drought, which would reduce the southward cold airflow on the northeast of the Tibetan Plateau and strengthen the west wind from dry central Asia on the south of the plateau. As a result, a larger area of the southwest region in China was controlled by the warm and dry air mass, which was acting against precipitation. The results will be of reference value to the drought forecast for Southwest China, and also encourage further research about how the Tibetan Plateau influence the climate on its eastern side.
Hyperspectral image classification (HSIC) is one of the most important research topics in the field of remote sensing. However, it is difficult to label hyperspectral data, which limits the ...improvement of classification performance of hyperspectral images in the case of small samples. To alleviate this problem, in this paper, a dual-branch network which combines cross-channel dense connection and multi-scale dual aggregated attention (CDC_MDAA) is proposed. On the spatial branch, a cross-channel dense connections (CDC) module is designed. The CDC can effectively combine cross-channel convolution with dense connections to extract the deep spatial features of HSIs. Then, a spatial multi-scale dual aggregated attention module (SPA_MDAA) is constructed. The SPA_MDAA adopts dual autocorrelation for attention modeling to strengthen the differences between features and enhance the ability to pay attention to important features. On the spectral branch, a spectral multi-scale dual aggregated attention module (SPE_MDAA) is designed to capture important spectral features. Finally, the spatial spectral features are fused, and the classification results are obtained. The experimental results show that the classification performance of the proposed method is superior to some state-of-the-art methods in small samples and has good generalization.
This article presents an overview on carbon chemical structure transformation to understand kerogen thermal decomposition based on the chemical structure of kerogen. Formation of kerogen is ...highlighted to distinguish the typical types of kerogen containing in oil shale and coal. The oil production potential for oil shale and coal is found to little correlate with their organic amounts but to depend on the quality or chemical structure of organic matters. Aliphatic and aromatic carbons in kerogen are correlative with the yield of oil and carbon residue from Fischer Assay retorting, respectively. The aliphatic carbon moieties largely produce oil and gas, while aromatic carbon portion is apt to be converted directly to carbon residue during kerogen pyrolysis process. On this basis, an updated lumped mechanism model is proposed for viewing kerogen pyrolysis and provides a basis for understanding the transformation of carbon chemical structures. Further quantization and analysis conclude that: 1) 10–20% aliphatic carbon leaves in carbon residue as methyl groups and methylene bridges attached to aromatic rings, 2) 45–80% aliphatic carbon is directly distillated into oil, and 3) 15–40% aliphatic carbon is aromatized into aromatic carbon. The aromatization degree of aliphatic carbon varies with secondary reactions and its intrinsic chemical structure (alkyl chains, naphthenic and hydroaromatic hydrocarbons). Thus, the article justifies that primary pyrolysis determines the potentially maximal oil yield according to original carbon chemical structure, while the subsequent secondary reactions should be selective and minimized to determine the final oil yield and quality.
Textile thermoregulation and thermal protection are crucial for human health and safety. Individual thermophysiological comfort control and flame retardancy are lacking in traditional garments. Novel ...nanomaterial innovations have solved these limitations and have facilitated the development of next‐generation intelligent textiles. Smart textiles based on graphene and graphene derivatives material have attracted substantial attention owing to its superior electrical conductivity, high thermal conductivity, and flexibility. This review provides an overview of the current progress on the smart textiles using graphene and graphene derivative material with a focus on personal thermal management and flame retardancy. It covers mechanics, material developments, fabric designs, and on‐body applications, offering a comprehensive knowledge and scope of the entire area. Innovations in chemistry and materials with worldwide collaboration will push the frontiers of graphene‐based smart textiles, promoting the development of genuine commercial goods on the market.
Personal thermal management and fire retardancy are the two important aspects of next generation smart textiles, and graphene and its derivatives have the capability to enable both of them. It this review, it is provided an overview of the current progress in this field, and some critical thoughts and perspectives for future studies.
Introduction: Diabetic nephropathy (DN) is a serious complication of diabetes mellitus and is considered to be a sterile inflammatory disease. Increasing evidence suggest that pyroptosis and ...subsequent inflammatory response play a key role in the pathogenesis of DN. However, the underlying cellular and molecular mechanisms responsible for pyroptosis in DN are largely unknown. Methods: The rat models of DN were successfully established by single 65 mg/kg streptozotocin treatment. Glomerular mesangial cells were exposed to 30 mmol/L high glucose media for 48 h to mimic the DN environment in vitro. Gene and protein expressions were determined by quantitative real-time PCR and Western blot. Cell viability and pyroptosis were measured by MTT assay and flow cytometry analysis, respectively. The relationship between lncRNA NEAT1, miR-34c, and Nod-like receptor protein-3 (NLRP3) was confirmed by luciferase reporter assay. Results: We found that upregulation of NEAT1 was associated with the increase of pyroptosis in DN models. miR-34c, as a target gene of NEAT1, mediated the effect of NEAT1 on pyroptosis in DN by regulating the expression of NLRP3 as well as the expressions of caspase-1 and interleukin-1β. Either miR-34c inhibition or NLRP3 overexpression could reverse the accentuation of pyroptosis and inflammation by sh-NEAT1 transfection in the in vitro model of DN. Conclusions: Our findings suggested NEAT1 and its target gene miR-34c regulated cell pyroptosis via mediating NLRP3 in DN, providing new insights into understanding the molecular mechanisms of pyroptosis in the pathogenesis of DN.
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