46 research outputs found

    TransFusionOdom: Interpretable Transformer-based LiDAR-Inertial Fusion Odometry Estimation

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    Multi-modal fusion of sensors is a commonly used approach to enhance the performance of odometry estimation, which is also a fundamental module for mobile robots. However, the question of \textit{how to perform fusion among different modalities in a supervised sensor fusion odometry estimation task?} is still one of challenging issues remains. Some simple operations, such as element-wise summation and concatenation, are not capable of assigning adaptive attentional weights to incorporate different modalities efficiently, which make it difficult to achieve competitive odometry results. Recently, the Transformer architecture has shown potential for multi-modal fusion tasks, particularly in the domains of vision with language. In this work, we propose an end-to-end supervised Transformer-based LiDAR-Inertial fusion framework (namely TransFusionOdom) for odometry estimation. The multi-attention fusion module demonstrates different fusion approaches for homogeneous and heterogeneous modalities to address the overfitting problem that can arise from blindly increasing the complexity of the model. Additionally, to interpret the learning process of the Transformer-based multi-modal interactions, a general visualization approach is introduced to illustrate the interactions between modalities. Moreover, exhaustive ablation studies evaluate different multi-modal fusion strategies to verify the performance of the proposed fusion strategy. A synthetic multi-modal dataset is made public to validate the generalization ability of the proposed fusion strategy, which also works for other combinations of different modalities. The quantitative and qualitative odometry evaluations on the KITTI dataset verify the proposed TransFusionOdom could achieve superior performance compared with other related works.Comment: Submitted to IEEE Sensors Journal with some modifications. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

    Deep3DSaliency: Deep Stereoscopic Video Saliency Detection Model by 3D Convolutional Networks

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    Robustness analysis of pedestrian detectors for surveillance

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    To obtain effective pedestrian detection results in surveillance video, there have been many methods proposed to handle the problems from severe occlusion, pose variation, clutter background, and so on. Besides detection accuracy, a robust surveillance video system should be stable to video quality degradation by network transmission, environment variation, and so on. In this paper, we conduct the research on the robustness of pedestrian detection algorithms to video quality degradation. The main contribution of this paper includes the following three aspects. First, a large-scale distorted surveillance video data set (DSurVD) is constructed from high-quality video sequences and their corresponding distorted versions. Second, we design a method to evaluate detection stability and a robustness measure called robustness quadrangle, which can be adopted to the visualize detection accuracy of pedestrian detection algorithms on high-quality video sequences and stability with video quality degradation. Third, the robustness of seven existing pedestrian detection algorithms is evaluated by the built DSurVD. Experimental results show that the robustness can be further improved for existing pedestrian detection algorithms. In addition, we provide much in-depth discussion on how different distortion types influence the performance of pedestrian detection algorithms, which is important to design effective pedestrian detection algorithms for surveillance.Published versio

    Efficient all polymer solar cells employing donor polymer based on benzo[1,2-b:4,5-b’]dithiophene unit

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    We reported all polymer solar cells (all-PSCs) employing BDT-based donor–acceptor (D–A) polymers composed of benzo[1,2-b:4,5-b’]dithiophene (BDT) and thiadiazolo[3,4-c]pyridine (PyTZ) (PBPT-8 and PBPT-12) as donor and NDI-based n-type polymer Poly{[N,N’-bis(2-octyldodecyl)-naphthalene-1,4,5,8-bis(dicarboximide)-2,6-diyl]-alt-5,5’-(2,2’-bithiophene)} (P(NDI2OD-T2)) (N2200) as acceptor. The influence of thermal annealing on the performance of all-PSCs was systematically investigated and discussed. It was found that the pre-annealing of the active blend films could significantly improve the all-PSCs performance. Both PBPT-8/PBPT-12:N2200 systems can deliver promising PCEs (4.12% and 4.25%) at the optimal annealing temperature of 160 oC due to the promoted film quality and charge transport properties. Morphology investigation and carrier mobility measurements have been carried out to analyze the effect of thermal annealing. This study suggests that BDT-based polymer:N2200 systems can be promising candidates for all-PSCs, with thermal annealing as an effective approach to promote the device performance

    Insights into the Allosteric Effect of SENP1 Q597A Mutation on the Hydrolytic Reaction of SUMO1 via an Integrated Computational Study

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    Small ubiquitin-related modifier (SUMO)-specific protease 1 (SENP1) is a cysteine protease that catalyzes the cleavage of the C-terminus of SUMO1 for the processing of SUMO precursors and deSUMOylation of target proteins. SENP1 is considered to be a promising target for the treatment of hepatocellular carcinoma (HCC) and prostate cancer. SENP1 Gln597 is located at the unstructured loop connecting the helices α4 to α5. The Q597A mutation of SENP1 allosterically disrupts the hydrolytic reaction of SUMO1 through an unknown mechanism. Here, extensive multiple replicates of microsecond molecular dynamics (MD) simulations, coupled with principal component analysis, dynamic cross-correlation analysis, community network analysis, and binding free energy calculations, were performed to elucidate the detailed mechanism. Our MD simulations showed that the Q597A mutation induced marked dynamic conformational changes in SENP1, especially in the unstructured loop connecting the helices α4 to α5 which the mutation site occupies. Moreover, the Q597A mutation caused conformational changes to catalytic Cys603 and His533 at the active site, which might impair the catalytic activity of SENP1 in processing SUMO1. Moreover, binding free energy calculations revealed that the Q597A mutation had a minor effect on the binding affinity of SUMO1 to SENP1. Together, these results may broaden our understanding of the allosteric modulation of the SENP1−SUMO1 complex

    Source Apportionment and Health Risk Assessment of Heavy Metals in Karst Water from Abandoned Mines in Zhangqiu, China

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    This study investigated the hydrochemical characteristics and human health risks of groundwater in a pollution accident site. By collecting 27 samples, the content of the heavy metal(oid)s (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) was tested, statistical analysis of heavy metal ion concentration was conducted, and the Nemerow comprehensive contamination index was determined. The health risk assessment was conducted based on the test results, and correlation analysis, as well as principal component analysis, were used to resolve the possible sources of heavy metal contamination. The results showed that the total hardness and total dissolved solids were significantly higher, and the potassium permanganate index and ammonia nitrogen content were higher in some samples. The heavy metal content was higher than the Class III groundwater quality standard (GB/T14848-2017). The health risk assessment showed that the total health risks posed by the eight heavy metal(oid)s in the study area through drinking water and dermal contact exceeded the maximum acceptable level. In general, the health risk is slightly higher for adults than for children. The groundwater in the abandoned mines has unsafe levels of heavy metal(oid)s for human health, but the normal drinking water remains safe for surrounding residents. Carcinogenic heavy metal(oid)s are the main source of health risks in the region, and the impact of Cr on human health requires further attention

    Alkenyl Carboxylic Acid: Engineering the Nanomorphology in Polymer–Polymer Solar Cells as Solvent Additive

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    We have investigated a series of commercially available alkenyl carboxylic acids with different alkenyl chain lengths (<i>trans</i>-2-hexenoic acid (CA-6), <i>trans</i>-2-decenoic acid (CA-10), 9-tetradecenoic acid (CA-14)) for use as solvent additives in polymer–polymer non-fullerene solar cells. We systematically investigated their effect on the film absorption, morphology, carrier generation, transport, and recombination in all-polymer solar cells. We revealed that these additives have a significant impact on the aggregation of polymer acceptor, leading to improved phase segregation in the blend film. This in-depth understanding of the additives effect on the nanomorphology in all-polymer solar cell can help further boost the device performance. By using CA-10 with the optimal alkenyl chain length, we achieved fine phase separation, balanced charge transport, and suppressed recombination in all-polymer solar cells. As a result, an optimal power conversion efficiency (PCE) of 5.71% was demonstrated which is over 50% higher than that of the as-cast device (PCE = 3.71%) and slightly higher than that of devices with DIO treatment (PCE = 5.68%). Compared with widely used DIO, these halogen-free alkenyl carboxylic acids have a more sustainable processing as well as better performance, which may make them more promising candidates for use as processing additives in organic non-fullerene solar cells
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