DC-SPP-YOLO: Dense Connection and Spatial Pyramid Pooling Based YOLO for Object Detection

Although YOLOv2 approach is extremely fast on object detection; its backbone network has the low ability on feature extraction and fails to make full use of multi-scale local region features, which restricts the improvement of object detection accuracy. Therefore, this paper proposed a DC-SPP-YOLO (Dense Connection and Spatial Pyramid Pooling Based YOLO) approach for ameliorating the object detection accuracy of YOLOv2. Specifically, the dense connection of convolution layers is employed in the backbone network of YOLOv2 to strengthen the feature extraction and alleviate the vanishing-gradient problem. Moreover, an improved spatial pyramid pooling is introduced to pool and concatenate the multi-scale local region features, so that the network can learn the object features more comprehensively. The DC-SPP-YOLO model is established and trained based on a new loss function composed of mean square error and cross entropy, and the object detection is realized. Experiments demonstrate that the mAP (mean Average Precision) of DC-SPP-YOLO proposed on PASCAL VOC datasets and UA-DETRAC datasets is higher than that of YOLOv2; the object detection accuracy of DC-SPP-YOLO is superior to YOLOv2 by strengthening feature extraction and using the multi-scale local region features.Comment: 23 pages, 9 figures, 9 table

Multi-Grained Angle Representation for Remote Sensing Object Detection

Arbitrary-oriented object detection (AOOD) plays a significant role for image understanding in remote sensing scenarios. The existing AOOD methods face the challenges of ambiguity and high costs in angle representation. To this end, a multi-grained angle representation (MGAR) method, consisting of coarse-grained angle classification (CAC) and fine-grained angle regression (FAR), is proposed. Specifically, the designed CAC avoids the ambiguity of angle prediction by discrete angular encoding (DAE) and reduces complexity by coarsening the granularity of DAE. Based on CAC, FAR is developed to refine the angle prediction with much lower costs than narrowing the granularity of DAE. Furthermore, an Intersection over Union (IoU) aware FAR-Loss (IFL) is designed to improve accuracy of angle prediction using an adaptive re-weighting mechanism guided by IoU. Extensive experiments are performed on several public remote sensing datasets, which demonstrate the effectiveness of the proposed MGAR. Moreover, experiments on embedded devices demonstrate that the proposed MGAR is also friendly for lightweight deployments.Comment: 13 pages, 9 figures, 14 table

Phylogenetic, Expression, and Bioinformatic Analysis of the ABC1

We studied 17 ABC1 genes in Populus trichocarpa, all of which contained an ABC1 domain consisting of about 120 amino acid residues. Most of the ABC1 gene products were located in the mitochondria or chloroplasts. All had a conserved VAVK-like motif and a DFG motif. Phylogenetic analysis grouped the genes into three subgroups. In addition, the chromosomal locations of the genes on the 19 Populus chromosomes were determined. Gene structure was studied through exon/intron organization and the MEME motif finder, while heatmap was used to study the expression diversity using EST libraries. According to the heatmap, PtrABC1P14 was highlighted because of the high expression in tension wood which related to secondary cell wall formation and cellulose synthesis, thus making a contribution to follow-up experiment in wood formation. Promoter cis-element analysis indicated that almost all of the ABC1 genes contained one or two cis-elements related to ABA signal transduction pathway and drought stress. Quantitative real-time PCR was carried out to evaluate the expression of all of the genes under abiotic stress conditions (ABA, CdCl2, high temperature, high salinity, and drought); the results showed that some of the genes were affected by these stresses and confirmed the results of promoter cis-element analysis

LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images

A few lightweight convolutional neural network (CNN) models have been recently designed for remote sensing object detection (RSOD). However, most of them simply replace vanilla convolutions with stacked separable convolutions, which may not be efficient due to a lot of precision losses and may not be able to detect oriented bounding boxes (OBB). Also, the existing OBB detection methods are difficult to constrain the shape of objects predicted by CNNs accurately. In this paper, we propose an effective lightweight oriented object detector (LO-Det). Specifically, a channel separation-aggregation (CSA) structure is designed to simplify the complexity of stacked separable convolutions, and a dynamic receptive field (DRF) mechanism is developed to maintain high accuracy by customizing the convolution kernel and its perception range dynamically when reducing the network complexity. The CSA-DRF component optimizes efficiency while maintaining high accuracy. Then, a diagonal support constraint head (DSC-Head) component is designed to detect OBBs and constrain their shapes more accurately and stably. Extensive experiments on public datasets demonstrate that the proposed LO-Det can run very fast even on embedded devices with the competitive accuracy of detecting oriented objects.Comment: 15 page

Metal-Organic Framework Supporting Fe3O4 Prepared by Microwave in Couple With NTP to Eliminate VOCs From Biofuel

In the production process of briquette biofuel, terpenes such as pinene and camphene, as well as non-terpenoid VOCs such as formic acid, acetaldehyde, and benzene, are generated during conditioning, drying and other procedures. Different catalysts of Fe3O4 supported by CoFe-ZIFs were prepared by thermal dissolution method and microwave dissolution method with changing the doping amount of iron. The structures of these catalysts were characterized by X-ray Diffraction (XRD), Fourier transform infrared spectrometer Fourier-Transformed InfraRed, thermogravimetric analysis (TG), and scanning electron microscope and the catalytic performance for α-pinene was tested on a fixed bed. The results show that, compared with the thermal dissolution method, the microwave dissolution method was easier to induce iron to form Fe3O4 grains with higher activity. At the same time, the grains did not affect the formation of CoFe-ZIFs structure. Fe3O4 supported on CoFe-ZIFs enhanced the catalytic activity and energy efficiency of the catalysts for α-pinene. Among the test samples, CoFe14-ZIF-W prepared by microwave method with Fe doping 14% mol exhibited the stable structural characteristics and the highest catalytic efficiency of 94.3% and energy efficiency of 8.11 g·kWh−1, which provided a further possibility of practical application for the removal of VOCs from biomass

Surface roughness characteristics effects on fluid load capability of tilt pad thrust bearings with water lubrication

Abstract The effects of surface roughness characteristics on the fluid load capacity of tilt pad thrust bearings with water lubrication were studied by the average flow model. The flow factors utilized in the average flow model were simulated with various surface roughness parameters including skewness, kurtosis and the roughness directional pattern. The results indicated that the fluid load capacity was not only affected by the RMS roughness but also by the surface roughness characteristics. The fluid load capacity was dramatically affected by the roughness directional pattern. The skewness had a lower effect than the roughness directional pattern. The kurtosis had no notable effect on the fluid load capacity. It was possible for the fluid load capacity of the tilt pad thrust bearings to be improved by the skewness and roughness direction pattern control

High-Altitude Balloon-Based Sensor System Design and Implementation

As a kind of large-scale unmanned aerial vehicle, a high-altitude balloon can carry a large load up to tens of kilometers in the near space for a long time, which brings a new way for the stratosphere atmospheric detection. In order to provide a suitable working environment for the near-space detection load, it is necessary to design a sensor system based on a high-altitude balloon, which is used to provide environmental temperature, height position, and attitude information, current working, and video surveillance. The high-altitude balloon-based sensor system designed in this paper had participated in the near-space flight experiment, whose total flight time was 30 h and 53 min, and the horizontal flight time was 28 h and 58 min crossing the day and night. The high-altitude balloon-based sensor system had withstood the severe environment of the near-space during the day and night, providing accurate temperature measurement, real-time altitude position and attitude data acquisition, reliable current monitoring, and comprehensive video surveillance. In the next three years, the high-altitude balloon-based sensor system developed in this paper will continue to participate in the experiment and provide support for more detection loads

High tension cyclic hydrocarbons synthesized from biomass-derived platform molecules for aviation fuels in two steps

Synthesizing ring structure aviation fuels from biomass-derived platform molecules is challenging, especially for bridged ring structure aviation fuels which are typically achieved from fossil-derived chemicals. Herein, we report the synthesis of a series of ring structure biofuels in two steps by a combination of a solvent-free Michael-cyclization reaction and a hydrodeoxygenation (HDO) reaction from lignocellulose-derived 5,5-dimethyl-1,3-cyclohexanedione. These biofuels are obtained with high overall yields up to 90%, which exhibit high densities of 0.81 g cm−3-0.88 g cm−3 and high volumetric neat heat of combustion (VNHOC) values of 36.0 MJ L−1-38.6 MJ L−1. More importantly, bridged-ring structure hydrocarbons can also be achieved in two steps by a combination of a Robinson annulation reaction and an HDO reaction to afford the final products at high overall yields up to 90%. The bridged-ring structure products have comparable high densities and high VNHOC values to the best artificial fuel JP-10 (0.94 g cm−3 and 39.6 MJ L−1). The results demonstrate a promising way for the synthesis of high-density aviation fuels with different fuel properties at high yields

Multimodal Collaboration Networks for Geospatial Vehicle Detection in Dense, Occluded, and Large-Scale Events

In large-scale disaster events, the planning of optimal rescue routes depends on the object detection ability at the disaster scene, with one of the main challenges being the presence of dense and occluded objects. Existing methods, which are typically based on the RGB modality, struggle to distinguish targets with similar colors and textures in crowded environments and are unable to identify obscured objects. To this end, we first construct two multimodal dense and occlusion vehicle detection datasets for large-scale events, utilizing RGB and height map modalities. Based on these datasets, we propose a multimodal collaboration network for dense and occluded vehicle detection, MuDet for short. MuDet hierarchically enhances the completeness of discriminable information within and across modalities and differentiates between simple and complex samples. MuDet includes three main modules: Unimodal Feature Hierarchical Enhancement (Uni-Enh), Multimodal Cross Learning (Mul-Lea), and Hard-easy Discriminative (He-Dis) Pattern. Uni-Enh and Mul-Lea enhance the features within each modality and facilitate the cross-integration of features from two heterogeneous modalities. He-Dis effectively separates densely occluded vehicle targets with significant intra-class differences and minimal inter-class differences by defining and thresholding confidence values, thereby suppressing the complex background. Experimental results on two re-labeled multimodal benchmark datasets, the 4K-SAI-LCS dataset, and the ISPRS Potsdam dataset, demonstrate the robustness and generalization of the MuDet