ISBDD model for classification of hyperspectral remote sensing imagery

The diverse density (DD) algorithm was proposed to handle the problem of low classification accuracy when training samples contain interference such as mixed pixels. The DD algorithm can learn a feature vector from training bags, which comprise instances (pixels). However, the feature vector learned by the DD algorithm cannot always effectively represent one type of ground cover. To handle this problem, an instance space-based diverse density (ISBDD) model that employs a novel training strategy is proposed in this paper. In the ISBDD model, DD values of each pixel are computed instead of learning a feature vector, and as a result, the pixel can be classified according to its DD values. Airborne hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor and the Push-broom Hyperspectral Imager (PHI) are applied to evaluate the performance of the proposed model. Results show that the overall classification accuracy of ISBDD model on the AVIRIS and PHI images is up to 97.65% and 89.02%, respectively, while the kappa coefficient is up to 0.97 and 0.88, respectively

Bacterial growth, detachment and cell size control on polyethylene terephthalate surfaces.

In medicine and food industry, bacterial colonisation on surfaces is a common cause of infections and severe illnesses. However, the detailed quantitative information about the dynamics and the mechanisms involved in bacterial proliferation on solid substrates is still lacking. In this study we investigated the adhesion and detachment, the individual growth and colonisation, and the cell size control of Escherichia coli (E. coli) MG1655 on polyethylene terephthalate (PET) surfaces. The results show that the bacterial growth curve on PET exhibits the distinct lag and log phases, but the generation time is more than twice longer than in bulk medium. Single cells in the lag phase are more likely to detach than clustered ones in the log phase; clustered bacteria in micro-colonies have stronger adhesive bonds with surfaces and their neighbours with the progressing colonisation. We show that the cell size is under the density-dependent pathway control: when the adherent cells are at low density, the culture medium is responsible for coordinating cell division and cell size; when the clustered cells are at high population density, we demonstrate that the effect of quorum sensing causes the cell size decrease as the cell density on surfaces increases.This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/srep1515

Vortex Induced Vibrations Of Slender Marine Structures: Inverse Analysis

With the development of the offshore engineering and the increasing of water depth, VIV (vortex induced vibrations) becomes a big challenge of the design of slender marine structures. In engineering field, we use some empirical VIV prediction codes like VIVAVA and Shear 7 to predict VIV. A key issue for using the codes is to establish a data base for hydrodynamic coefficients. Such coefficients have so far been found from experiments. An alternative way to get the force coefficients is to measure the response in flexible beams in current, and get the local force using some mathematical calculation method, which is called inverse analysis. In this project, we analyzed 44 NDP riser model tests. First we apply inverse analysis method to estimate the excitation force coefficients for one test and make comparison with the previous results from the rigid pipe model test. And we can find out the inverse analysis method is quite an efficient way to calculate the force coefficients. Second we compare the results with the existing models in VIVAVA for excitation and damping coefficients. We can see that the model in VIVAVA can give more reasonable results of the damping coefficients compared with inverse analysis when the non-dimensional frequency is outside the excitation range.Third we calculate the fatigue damage from the varying frequency components in order to find the contribution from the primary cross flow frequency and higher order frequency. We find that the fatigue generated by higher order frequency components is as important as that from the primary cross flow frequency component and cannot be neglected. We can introduce a parameter which can be used to find out the total fatigue damage from the primary frequency fatigue damage

Ultrafast Spin-To-Charge Conversion at the Surface of Topological Insulator Thin Films

Strong spin-orbit coupling, resulting in the formation of spin-momentum-locked surface states, endows topological insulators with superior spin-to-charge conversion characteristics, though the dynamics that govern it have remained elusive. Here, we present an all-optical method that enables unprecedented tracking of the ultrafast dynamics of spin-to-charge conversion in a prototypical topological insulator Bi$_2$Se$_3$/ferromagnetic Co heterostructure, down to the sub-picosecond timescale. Compared to pure Bi$_2$Se$_3$ or Co, we observe a giant terahertz emission in the heterostructure than originates from spin-to-charge conversion, in which the topological surface states play a crucial role. We identify a 0.12-picosecond timescale that sets a technological speed limit of spin-to-charge conversion processes in topological insulators. In addition, we show that the spin-to-charge conversion efficiency is temperature independent in Bi$_2$Se$_3$ as expected from the nature of the surface states, paving the way for designing next-generation high-speed opto-spintronic devices based on topological insulators at room temperature.Comment: 19 pages, 4 figure

Acid monolayer functionalized iron oxide nanoparticles as catalysts for carbohydrate hydrolysis

Superparamagnetic iron oxide nanoparticles were functionalized with a quasi-monolayer of 11-sulfoundecanoic acid and 10-phosphono-1-decanesulfonic acid ligands to create separable solid acid catalysts. The ligands are bound through carboxylate or phosphonate bonds to the magnetite core. The ligand-core bonding surface is separated by a hydrocarbon linker from an outer surface with exposed sulfonic acid groups. The more tightly packed monolayer of the phosphonate ligand corresponded to a higher sulfonic acid loading by weight, a reduced agglomeration of particles, a greater tendency to remain suspended in solution in the presence of an external magnetic field, and a higher catalytic activity per sulfonic acid group. The particles were characterized by thermogravimetric analysis (TGA), transmission electron microscopy (TEM), potentiometric titration, diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), inductively coupled plasma optical emission spectrometry (ICP-OES), and dynamic light scattering (DLS). In sucrose catalysis reactions, the phosphonic–sulfonic nanoparticles (PSNPs) were seen to be incompletely recovered by an external magnetic field, while the carboxylic–sulfonic nanoparticles (CSNPs) showed a trend of increasing activity over the first four recycle runs. The activity of the acid-functionalized nanoparticles was compared to the traditional solid acid catalyst Amberlyst-15 for the hydrolysis of starch in aqueous solution. Catalytic activity for starch hydrolysis was in the order PSNPs > CSNPs > Amberlyst-15. Monolayer acid functionalization of iron oxides presents a novel strategy for the development of recyclable solid acid catalysts

An investigation into in-cylinder tumble flow characteristics with variable valve lift in a gasoline engine

In this paper, the investigation into in-cylinder tumble flow characteristics with reduced Maximum Valve Lifts (MVL) is presented. The experimental work was conducted in a modified four-valve Spark-Ignition (SI) test engine, with optical accesses for measuring in-cylinder air motion in the vertical direction. Three different MVL of 6.8 mm, 4.0 mm and 1.7 mm were tested and Particle Image Velocimetry (PIV) was employed for those measurements. Measurement results were analysed by examining the tumble flow field, the tumble ratio variation and the fluctuating kinetic energy distribution. Meanwhile, a numerical analysis method for detecting the vortex centre was developed. From results of the vortex centre distribution, the cyclic variation of the in-cylinder flow was explored. The phase-averaged flow fields show that higher MVLs could produce stronger vertical flows which turn more toward to the piston top and finally are possible to form big scale tumble flow structure. Although lower MVLs create a higher tumble ratio when the piston is close to the Bottom Dead Centre (BDC), higher MVLs substantially produce higher tumble ratios when the piston is moving close to the Top Dead Centre (TDC). In terms of kinetic energy, lower MVLs result in higher values including higher total kinetic energy and higher fluctuating energy. Finally, the vortex centres results demonstrate lower MVLs could enhance cycle-to-cycle variation due to the weakened tumble vortex

Underwater target detection based on improved YOLOv7

Underwater target detection is a crucial aspect of ocean exploration. However, conventional underwater target detection methods face several challenges such as inaccurate feature extraction, slow detection speed and lack of robustness in complex underwater environments. To address these limitations, this study proposes an improved YOLOv7 network (YOLOv7-AC) for underwater target detection. The proposed network utilizes an ACmixBlock module to replace the 3x3 convolution block in the E-ELAN structure, and incorporates jump connections and 1x1 convolution architecture between ACmixBlock modules to improve feature extraction and network reasoning speed. Additionally, a ResNet-ACmix module is designed to avoid feature information loss and reduce computation, while a Global Attention Mechanism (GAM) is inserted in the backbone and head parts of the model to improve feature extraction. Furthermore, the K-means++ algorithm is used instead of K-means to obtain anchor boxes and enhance model accuracy. Experimental results show that the improved YOLOv7 network outperforms the original YOLOv7 model and other popular underwater target detection methods. The proposed network achieved a mean average precision (mAP) value of 89.6% and 97.4% on the URPC dataset and Brackish dataset, respectively, and demonstrated a higher frame per second (FPS) compared to the original YOLOv7 model. The source code for this study is publicly available at https://github.com/NZWANG/YOLOV7-AC. In conclusion, the improved YOLOv7 network proposed in this study represents a promising solution for underwater target detection and holds great potential for practical applications in various underwater tasks

PI parameter tuning of converters for sub-synchronous interactions existing in grid-connected DFIG wind turbines

As a clean energy, wind power has been extensively exploited in the past few years. However, oscillations in wind turbines, particularly those from controllers, could severely affect the stability of power systems. Therefore, oscillation suppression is a recent research focus. Based on the small-signal model eigenvalues and participation factors, this paper detects the sub-synchronous interactions (SSI) mainly determined by converters' PI parameters in a grid-connected doubly fed induction generator (DFIG). With the aim of oscillation restraint, a novel optimization model with the reference-point based non-dominated sorting genetic algorithm (NSGA-III) and the t-distributed stochastic neighbour embedding (t-SNE) is developed to explore and visualize optimal ranges of PI parameters, facilitating the selection of the appropriate PI parameters to augment the damping. Additionally, to study the adaptability of the optimal PI parameters, interactions performance of the system that uses optimal parameters is studied with different output levels of the wind turbine. Finally, a time domain simulation and a practical experiment are conducted to demonstrate the effectiveness of the proposed approach. Results illustrate that the SSI of a grid-connected DFIG is suppressed by the optimization model. This study is highly beneficial to power system operators in integrating wind power and maintaining system stability.</p

Durability Environmental Regionalization for Concrete Structures

Environment is the external factor that affects the durability of concrete structures. Buildings in different regions with different climates will respond to durability deterioration in different ways. For macroenvironmental regionalization, the dominant factor analysis method of the climatic zonation was applied into the environmental regionalization in this paper. Based on the environmental characteristics in China and the effect of environmental factor on the durability of concrete structure, the proper regionalization indexes are chosen, and the environmental regionalization is made. For microenvironmental regionalization, fuzzy set and rough set theories were used in date mining on discrete measured data, and the weight determination of various factors affecting durability was transformed into evaluation of the significance of attributes among rough sets. The method of durability environmental regionalization is established by analyzing the degree of influence that various factors have on the durability of concrete structures. The result of durability environmental regionalization for concrete structures in Shenzhen city shows that the proposed approach is reasonable