Fine-Grained Classification of Remote Sensing Ship Images Based on Improved VAN

The remote sensing ships’fine-grained classification technology makes it possible to identify certain ship types in remote sensing images,and it has broad application prospects in civil and military fields.However,the current model does not examine the properties of ship targets in remote sensing images with mixed multi-granularity features and a complicated backdrop.There is still an opportunity for future enhancement of the classification impact.To solve the challenges brought by the above characteristics,this paper proposes a Metaformer and Residual fusion network based on Visual Attention Network(VAN-MR)for fine-grained classification tasks.For the complex background of remote sensing images,the VAN-MR model adopts the parallel structure of large kernel attention and spatial attention to enhance the model’s feature extraction ability of interest targets and improve the classification performance of remote sensing ship targets.For the problem of multi-grained feature mixing in remote sensing images,the VAN-MR model uses a Metaformer structure and a parallel network of residual modules to extract ship features.The parallel network has different depths,considering both high-level and lowlevel semantic information.The model achieves better classification performance in remote sensing ship images with multi-granularity mixing.Finally,the model achieves 88.73%and 94.56%accuracy on the public fine-grained ship collection-23(FGSC-23)and FGSCR-42 datasets,respectively,while the parameter size is only 53.47 M,the floating point operations is 9.9 G.The experimental results show that the classification effect of VAN-MR is superior to that of traditional CNNs model and visual model with Transformer structure under the same parameter quantity.

Cancer incidence and patient survival rates among the residents in the Pudong New Area of Shanghai between 2002 and 2006

With the growing threat of malignancy to health,it is necessary to analyze cancer incidence and patient survival rates among the residents in Pudong New Area of Shanghai to formulate better cancer prevention strategies.A total of 43,613 cancer patients diagnosed between 2002 and 2006 were recruited from the Pudong New Area Cancer Registry.The incidence,observed survival rate,and relative survival rate of patients grouped by sex,age,geographic area,and TNM stage were calculated using the Kaplan-Meier,life table,and Ederer II methods,respectively.Between 2002 and 2006,cancer incidence in Pudong New Area was 349.99 per 100,000 person-years,and the 10 most frequently diseased sites were the lung,stomach,colon and rectum,liver,breast,esophagus,pancreas,brain and central nervous system,thyroid,and bladder.For patients with cancers of the colon and rectum,breast,thyroid,brain and central nervous system,and bladder,the 5-year relative survival rate was greater than 40%,whereas patients with cancers of the liver and pancreas had a 5-year relative survival rate of less than 10%.The 1-year to 5-year survival rates for patients grouped by sex,age,geographic area,and TNM stage differed significantly(all P<0.001).Our results indicate that cancer incidence and patient survival in Pudong New Area vary by tumor type,sex,age,geographic area,and TNM stage.

An Adaptive Robust Approach to Modeling and Control of Flexible Arm Robots

In this paper, a novel adaptive robust approach to modeling and control of a class of flexible-arm robots subject to actuators unmodeled dynamics is proposed. It is shown how real-time signals measured from a dynamical system can be utilized to improve the accuracy of the mathematical model of flexible robots. Given the elasticity of the robot’s arms, flexible manipulators have both passive and active degrees of freedom. A nonlinear robust controller is designed for the active degrees of freedom to enable the robot to follow desired trajectories in the presence of actuators unmodeled dynamics. Furthermore, it is shown that under some feasible conditions, another nonlinear robust controller is designed for the passive degrees of freedom. Moreover, to use the system response for model extraction, two auxiliary signals are proposed to provide sufficient information for improving the accuracy of the dynamics of the system numerically. Additionally, two adaptive laws are proposed in each case to update the two introduced auxiliary signals. As a result, the controller controls the passive degrees of freedom after the active degrees of freedom converge to their desired trajectories. Simultaneously, the information collected from the system to update the auxiliary signals enhances the model accuracy. In the end, simulation results are presented to verify the performance of the proposed controller.

Enabling Highly Efficient k-Means Computations on the SW26010 Many-Core Processor of Sunway TaihuLight

With the advent of the big data era,the amounts of sampling data and the dimensions of data features are rapidly growing.It is highly desired to enable fast and efficient clustering of unlabeled samples based on feature similarities. As a fundamental primitive for data clustering,the k-means operation is receiving increasingly more attentions today.To achieve high performance k-means computations on modern multi-core/many-core systems,we propose a matrix-based fused framework that can achieve high performance by conducting computations on a distance matrix and at the same time can improve the memory reuse through the fusion of the distance-matrix computation and the nearest centroids reduction.We implement and optimize the parallel k-means algorithm on the SW26010 many-core processor,which is the major horsepower of Sunway TaihuLight.In particular,we design a task mapping strategy for load-balanced task distribution,a data sharing scheme to reduce the memory footprint and a register blocking strategy to increase the data locality.Optimization techniques such as instruction reordering and double buffering are further applied to improve the sustained performance.Discussions on block-size tuning and performance modeling are also presented.We show by experiments on both randomly generated and real-world datasets that our parallel implementation of k-means on SW26010 can sustain a double-precision performance of over 348.1 Gflops,which is 46.9% of the peak performance and 84%of the theoretical performance upper bound on a single core group,and can achieve a nearly ideal scalability to the whole SW26010 processor of four core groups.Performance comparisons with the previous state-of-the-art on both CPU and GPU are also provided to show the superiority of our optimized k-means kernel.

Animal models for testing biomaterials in periodontal regeneration

Periodontitis is a prevalent oral disease. It can cause tooth loss and has a significant impact on patients’ quality of life. While existing treatments can only slow the progression of periodontitis, they are unable to achieve complete regeneration and functional reconstruction of periodontal tissues. As a result, regenerative therapies based on biomaterials have become a focal point of research in the field of periodontology. Despite numerous studies reporting the superiority of new materials in periodontal regeneration, limited progress has been made in translating these findings into clinical practice. This may be due to the lack of appropriate animal models to simulate the tissue defects caused by human periodontitis. This review aims to provide an overview of established animal models for periodontal regeneration, examine their advantages and limitations, and outline the steps for model construction. The objective is to determine the most relevant animal models for periodontal regeneration based on the hypothesis and expected outcomes.

Ultrawide-band optically transparent antidiffraction metamaterial absorber with a Thiessen-polygon metal-mesh shielding layer

Transparent absorbers, with a functional integration of broadband electromagnetic shielding, microwave camouflage,and optical transparency, have attracted increasing attention in the past decades. Metal mesh, an artificial, optically transparent, conducting material composed of periodic metallic gratings, is the optimal choice for the microwave shielding layer of transparent absorbers because of its excellent compatibility between high transparency and low resistance. However, the micrometer-level periodicity of metallic grating concentrates the diffraction of light, which degrades the imaging quality of cameras and sensors in common. In this study, we report on a generalized Thiessenpolygon-randomization method that prevents the concentration of the diffraction of light in periodic metallic grating and demonstrate an ultrawide-band optically transparent diffraction-immune metamaterial absorber. The absorber is constructed with a multilayer indium-tin-oxide-based metasurface and a Thiessen-polygon-randomized metal-mesh reflector. The lossy metasurface provides multimode absorption, whereas the Thiessen-polygon randomization prevents the concentration of the diffraction of light. The practical sample achieves a 10 dB absorptivity and shielding effectiveness over a range of 8–26.5 GHz, and the optical transparency is also preserved over the entire visible and near-infrared regions. The point spread function and field of view are both improved by using the antidiffraction absorber. Our study paves the way for the application of optically transparent electromagnetic devices, display, and optoelectronic integration in a more practical stage. ? 2023 Chinese Laser Press.

Smart Gait:A Gait Optimization Framework for Hexapod Robots

The current gait planning for legged robots is mostly based on human presets,which cannot match the flexible characteristics of natural mammals.This paper proposes a gait optimization framework for hexapod robots called Smart Gait.Smart Gait contains three modules:swing leg trajectory optimization,gait period&duty optimization,and gait sequence optimization.The full dynamics of a single leg,and the centroid dynamics of the overall robot are considered in the respective modules.The Smart Gait not only helps the robot to decrease the energy consumption when in locomotion,mostly,it enables the hexapod robot to determine its gait pattern transitions based on its current state,instead of repeating the formalistic clock-set step cycles.Our Smart Gait framework allows the hexapod robot to behave nimbly as a living animal when in 3D movements for the first time.The Smart Gait framework combines offline and online optimizations without any fussy data-driven training procedures,and it can run efficiently on board in real-time after deployment.Various experiments are carried out on the hexapod robot LittleStrong.The results show that the energy consumption is reduced by 15.9%when in locomotion.Adaptive gait patterns can be generated spontaneously both in regular and challenge environments,and when facing external interferences.

Functionalization of small black phosphorus nanoparticles for targeted imaging and photothermal therapy of cancer

Black phosphorus(BP) nanomaterials have attracted extensive attention due to their unique physical,chemical, and biological properties. In this study, small BP nanoparticles were synthesized and modified with dextran and poly(ethyleneimine) for functionalization with folic acid and cyanine 7. The functionalized BP nanoparticles exhibit excellent biocompatibility, stability, and near infrared optical properties for targeted imaging of tumors through photoacoustic imaging and near-infrared fluorescence imaging.They also display high photothermal conversion efficiency for photothermal therapy of cancer. This work demonstrates the potential of functionalized small BP nanoparticles as an emerging nanotheranostic agent for the diagnosis and treatment of cancer.

Fe atoms anchored on defective nitrogen doped hollow carbon spheres as efficient electrocatalysts for oxygen reduction reaction

Defective electrocatalysts,especially for intrinsic defective carbon,have aroused a wide concern owing to high spin and charge densities.However,the designated nitrogen species favorable for creating defects by the removal of nitrogen,and the influence of defects for the coordination structure of active site and oxygen reduction reaction(ORR)activity have not been elucidated.Herein,we designed and synthesized a pair of electrocatalysts,denoted as Fe-N/C and Fe-ND/C for coordination sites of atomic iron-nitrogen and iron-nitrogen/defect configuration embedded in hollow carbon spheres,respectively,through direct pyrolysis of their corresponding hollow carbon spheres adsorbed with Fe(acac)3.The nitrogen defects were fabricated via the evaporation of pyrrolic-N on nitrogen doped hollow carbon spheres.Results of comparative experiments between Fe-N/C and Fe-ND/C reveal that Fe-ND/C shows superior ORR activity with an onset potential of 30 mV higher than that of Fe-N/C.Fe-ND sites are more favorable for the enhancement of ORR activity.Density functional theory(DFT)calculation demonstrates that Fe-ND/C with proposed coordination structure of FeN_(4-x)(0<x<4)anchored by OH as axial ligand during ORR,weakens the strong binding of OH^(*)intermediate and promotes the desorption of OH^(*)as rate-determining step for ORR in alkaline electrolyte.Thus,Fe-ND/C electrocatalysts present much better ORR activity compared with that of Fe-N/C with proposed coordination structure of FeN_(4).

Terrain classification and adaptive locomotion for a hexapod robot Qingzhui

Legged robots have potential advantages in mobility compared with wheeled robots in outdoor environments. The knowledge of various ground properties and adaptive locomotion based on different surface materials plays an important role in improving the stability of legged robots. A terrain classification and adaptive locomotion method for a hexapod robot named Qingzhui is proposed in this paper. First, a force-based terrain classification method is suggested. Ground contact force is calculated by collecting joint torques and inertial measurement unit information. Ground substrates are classified with the feature vector extracted from the collected data using the support vector machine algorithm. Then, an adaptive locomotion on different ground properties is proposed. The dynamic alternating tripod trotting gait is developed to control the robot, and the parameters of active compliance control change with the terrain. Finally, the method is integrated on a hexapod robot and tested by real experiments. Our method is shown effective for the hexapod robot to walk on concrete, wood, grass, and foam. The strategies and experimental results can be a valuable reference for other legged robots applied in outdoor environments.

A Spatiotemporal Causality Based Governance Framework for Noisy Urban Sensory Data

Urban sensing is one of the fundamental building blocks of urban computing.It uses various types of sensors deployed in different geospatial locations to continuously and cooperatively monitor the natural and cultural environment in urban areas.Nevertheless,issues such as uneven distribution,low sampling rate and high failure ratio of sensors often make their readings less reliable.This paper provides an innovative framework to detect the noise data as well as to repair them from a spatial-temporal causality perspective rather than to deal with them inclividually.This can be achieved by connecting data through monitored objects,using the Skip-gram model to estimate spatial correlation and long shortterm memory to estimate temporal correlation.The framework consists of three major modules:1)a space embedded Bidirectional Long Short-Term Memory(BiLSTM)-based sequence labeling module to detect the noise data and the latent missing data;2)a space embedded BiLSTM-based sequence predicting module calculating the value of the missing data;3)an object characteristics fusion repairing module to correct the spatial and temporal dislocation sensory data.The approach is evaluated with real-world data collected by over 3000 electronic traffic bayonet devices in a citywide scale of a medium-sized city in China,and the result is superior to those of several referenced approaches.With a 12.9%improvement,in data accuracy over the raw data,the proposed framework plays a significant,role in various real-world use cases in urban governance,such as criminal investigation,traffic violation monitoring,and equipment maintenance.

A Simplified Control Method to Achieve Stable and Robust Quadrupedal Quasi-passive Walking with Compliant Legs

The principle of passive dynamic walking has drawn lots of attentions in the field of robotics for it provides a possibility to realize natural walking. However, stabilizing the quadrupedal passive walking remains challenging. In this paper, a novel control method is proposed to stabilize the quadrupedal quasi-passive walking. Inspired by biological concepts, this method treats the foreleg pair and hindleg pair as two bipedal walkers, and a virtual model controller is designed to maintain the quasi-passive walking of each bipedal walker independently. This control method was then verified by a planar quadrupedal model with compliant legs, which successfully achieved stable periodical walking gaits. It was found that although being con- trolled independently, the movement of fore and hind leg pairs still formed a time-invariant phase shift, showing remarkable resemblance to that of a walking horse. We fiarther analyzed the influences of varying factors on the gait characteristics and stability. These analyses show the control method is robust since it can stabilize the gaits within a wide range of leg compliance parameters and resist considerably large disturbances. In addition, the optimal ranges of the leg compliance parameters for the largest stability margin were also found in this study.

Landing control method of a lightweight four-legged landing and walking robot

The prober with an immovable lander and a movable rover is commonly used to explore the Moon’s surface.The rover can complete the detection on relatively flat terrain of the lunar surface well,but its detection efficiency on deep craters and mountains is relatively low due to the difficulties of reaching such places.A lightweight four-legged landing and walking robot called“FLLWR”is designed in this study.It can take off and land repeatedly between any two sites wherever on deep craters,mountains or other challenging landforms that are difficult to reach by direct ground movement.The robot integrates the functions of a lander and a rover,including folding,deploying,repetitive landing,and walking.A landing control method via compliance control is proposed to solve the critical problem of impact energy dissipation to realize buffer landing.Repetitive landing experiments on a five-degree-of-freedom lunar gravity testing platform are performed.Under the landing conditions with a vertical velocity of 2.1 m/s and a loading weight of 140 kg,the torque safety margin is 10.3%and 16.7%,and the height safety margin is 36.4%and 50.1%for the cases with or without an additional horizontal disturbance velocity of 0.4 m/s,respectively.The study provides a novel insight into the next-generation lunar exploration equipment.