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The central pattern generator (CPG) is widely applied in biped gait generation, and the particle swarm optimization (PSO) algorithm is commonly used to solve optimization problems for CPG network controllers. However, the canonical PSO algorithms fail to balance exploration and exploitation, resulting in reduced optimization accuracy and stability, decreasing the control effectiveness of CPG controllers. In order to address this issue, a balanced PSO (BPSO) algorithm is proposed, which achieves better performance by balancing the algorithm's exploration and exploitation capabilities. The BPSO algorithm's solving process consists of two phases: the free exploration phase (FEP), which emphasizes exploration, and the attention exploration phase (AEP), which emphasizes exploitation. The proportion of each phase during optimization is controlled by an adjustable parameter. The BPSO algorithm is subjected to qualitative, numerical, convergence, and statistical analyses based on 13 benchmark functions. The experimental results from the benchmark functions demonstrate that the BPSO algorithm outperforms other comparison algorithms. Finally, a linear walking optimization method for humanoid robots based on the BPSO algorithm is established and tested in the Webots simulator. Comparative results with two other optimization methods show that the BPSO‐based optimization method enables the robot to achieve greater walking distance and smaller lateral deviation within a fixed number of iterations. Compared to the other two methods, walking distance increases by at least 60.98% and lateral deviation decreases by at least 1.96%. This research contributes to enhancing the locomotion capabilities of CPG‐controlled humanoid robots, enriching biped gait optimization theory and promoting the application of CPG gait control methods in humanoid robots. [ABSTRACT FROM AUTHOR]

An event camera is a neuromimetic sensor inspired by the human retinal imaging principle, which has the advantages of high dynamic range, high temporal resolution, and low power consumption. Due to the interference of hardware and software and other factors, the event stream output from the event camera usually contains a large amount of noise, and traditional denoising algorithms cannot be applied to the event stream. To better deal with different kinds of noise and enhance the robustness of the denoising algorithm, based on the spatio-temporal distribution characteristics of effective events and noise, an event stream noise reduction and visualization algorithm is proposed. The event stream enters fine filtering after filtering the BA noise based on spatio-temporal density. The fine filtering performs time sequence analysis on the event pixels and the neighboring pixels to filter out hot noise. The proposed visualization algorithm adaptively overlaps the events of the previous frame according to the event density difference to obtain clear and coherent event frames. We conducted denoising and visualization experiments on real scenes and public datasets, respectively, and the experiments show that our algorithm is effective in filtering noise and obtaining clear and coherent event frames under different event stream densities and noise backgrounds. [ABSTRACT FROM AUTHOR]

Applying standard aerosol models for atmospheric correction in nearshore coastal waters introduces significant uncertainties due to their inability to accurately represent aerosol characteristics in these regions. To improve the accuracy of remote sensing reflectance ( R r s ) products in the nearshore waters of the Shandong Peninsula, this study develops an aerosol model based on aerosol data collected from the Mu Ping site in the coastal area of the Shandong Peninsula, enabling tailored atmospheric correction for this specific region. Given the pronounced seasonal variations in aerosol optical properties, monthly aerosol models were developed. The monthly aerosol model is derived using the average values of aerosol microphysical properties. Compared to the standard aerosol model, this model is more effective in characterizing the absorption and scattering characteristics of aerosols in the study area. Corresponding lookup tables for the aerosol model were created and integrated into the NIR-SWIR atmospheric correction algorithm. According to the accuracy evaluation indexes of RMSD, MAE, and UPD, it can be found that the atmospheric correction results of the aerosol model established in this paper are better than those of the standard aerosol model, especially in the 547 nm band. It demonstrates that the new aerosol model outperforms the standard model in atmospheric correction performance. With the increasing availability of aerosol observational data, the aerosol model is expected to become more accurate and applicable to other satellite missions. [ABSTRACT FROM AUTHOR]

Accurate atmospheric correction (AC) is essential in retrieving ocean and coastal water ecosystem products from ocean colour remote sensing data. Generally, the atmospheric correction algorithms that are based on Near Infrared (NIR) bands are effective in clear water, whereas the algorithm based on ultraviolet (UV) bands deliver accurate results in turbid water. A practical approach is to employ a UV-NIR combined algorithm that uses both UV and NIR bands for clear and turbid water, respectively. However, currently, the UV-NIR algorithm generates a data discontinuity pattern when a single threshold value is used, as the coastal water exhibits a continuous variability between clear and turbid states. To address this issue, we propose a 'clear and turbid water transition evenly combination' algorithm that utilizes UV and NIR bands of the Second Geostationary Ocean Colour Imager (GOCI-2). We evaluated the new algorithm's performance by comparing GOCI-2 images with in-situ data, and the results showed good agreement between the retrieved Remote Sensing Reflectance (${R_{rs}}$ R rs ) values at visible bands and the in-situ observations. The new AC algorithm can achieve better precision, with Mean Absolute Percentage Difference (MAPD) reduced by 6.6 95.3% and 5.2 18.9% compared with UV AC and the standard AC algorithm employed by the Korea Ocean Satellite Center (KOSC). [ABSTRACT FROM AUTHOR]

The Chinese Ocean Color and Temperature Scanner (COCTS) on-board the Chinese second ocean color satellite, HY-1B, obtained approximately 6 years of data between 2007 and 2013 in China coastal seas and the adjacent waters. However, its radiometric performance has hardly been analyzed, which confuses its applicability in ocean remote sensing. This study tracked the long-term radiometric responsivity trend of HY-1B COCTS based on a stable marine target. Firstly, we identified a temporally stable maritime site of 12° ~ 15°N and 116°~119°E according to the water and atmospheric optical properties using Aqua MODIS products. Then, the time-series of top-of-atmosphere (TOA) reflectance was obtained for each band of HY-1B COCTS and Aqua MODIS over this site according to the criteria of sun-target-view geometry. Finally, exponential or linear degradation models were built and used to adjust the radiometric levels of HY-1B COCTS. Results indicate that the radiometric performance exhibited continuous degradation for all bands at varying levels between 0.4% and 8.1% yr−1. The worst degradation occurred at 412 nm, with an annual average rate of 8.1%. The degradation at 443 nm reached 5.5% yr−1 following 412 nm. The radiometric performance at 490 nm, 520 nm, and 565 nm was relatively stable with a drift of ~3% yr−1. The 670 nm, 750 nm, and 865 nm bands remain most stable with the degradation of ~1% yr−1. Taking Terra MODIS as a reference, the temporal consistency of HY-1B COCTS was significantly improved for each band after radiometric adjustment. Cloud-free imageries between 2007 and 2013 showed relatively high spatial consistency. The bias of TOA reflectance was ~5% in visible bands and ~10% in near-infrared bands after degradation correction. These improvements confirm the application potentials of HY-1B COCTS in ocean remote sensing. [ABSTRACT FROM AUTHOR]

Study on the upper ocean is of great significance to the global climate change and carbon cycle. Lidar can be used to effectively detect depth-resolved optical properties of the ocean. However, both theory and experiment of oceanic lidar are limited by complex multiple scattering. Several progresses by Zhejiang University will be illustrated in this paper: 1) a polarized lidar system was developed, and a Monte Carlo model and a radiative transfer model were established (Zhou, et al. remote sensing, 2019; Zhou, et al. Journal of remote sensing, 2019; Xu, et al. and Liu, et al. Journal of remote sensing, 2019); 2) Cross validations are demonstrated to verify the availability of the lidar system and models (Liu, et al. IEEE TGRS, 2019); 3) phase function effects on backscatter and attenuation are studied considering multiple scattering, respectively (Liu, et al. Optics Express, 2019). Oceanic lidar is proven to have great potential in marine studies.