Technical Report for Argoverse Challenges on 4D Occupancy Forecasting

This report presents our Le3DE2E_Occ solution for 4D Occupancy Forecasting in Argoverse Challenges at CVPR 2023 Workshop on Autonomous Driving (WAD). Our solution consists of a strong LiDAR-based Bird's Eye View (BEV) encoder with temporal fusion and a two-stage decoder, which combines a DETR head and a UNet decoder. The solution was tested on the Argoverse 2 sensor dataset to evaluate the occupancy state 3 seconds in the future. Our solution achieved 18% lower L1 Error (3.57) than the baseline and got the 1 place on the 4D Occupancy Forecasting task in Argoverse Challenges at CVPR 2023

Sampling investigation and statistical analysis for mental problems of freshmen in Shandong Province

Abstract niversity stage, especially the freshmen stage, is a high incidence stage of students’ psychological problems. Effective sampling investigation and statistical analysis of freshmen’s mental health problems are conducive to solve freshmen’s problems and prevent further crises. In the past 4 years (2018–2021), using Chinese college students’ mental health screening scale, we have taken probability proportionate to size sampling investigations about the mental problems to 9882 freshmen in 45 public universities in Shandong Province near the end of their first semester. Based on these data, we conducted the comparison of anxiety and depression for post pandemic era vs. pre pandemic era by analysis of variance, and analysed the influencing factors for anxiety and depression by linear regression and canonical correlation analysis. The results indicate the extents of anxiety and depression in post pandemic era are significantly more severe than pre pandemic era. Inferiority complex, obsession and somatization are the main effect factors of anxiety and depression. To the best of our knowledge, this research is the first systematical investigation and analysis for the mental problems among freshmen in the whole Shandong Province before and after the epidemic. The research results are conducive for the mental health counseling and intervention of freshmen’s mental problems, and also helpful for policy making and prevention for psychological crisis management

Analytical and experimental studies of dragging hall anchors through rock berm

An analytical method is proposed in this paper to calculate the maximum embedded depth of a dragged Hall anchor when passing through rock berm and to thus define a minimum buried depth of pipelines in rock berm to prevent pipelines from being damaged by dragging anchors. The movement of a Hall anchor in rock berm is interpreted based on the equilibrium conditions for resisting and driving moments acting on the anchor. To verify the accuracy of the proposed analytical method, model tests were carried out by using three scaled Hall anchor models and dragging them through rock berm. The comparisons between the two studies show that the average value of their differences for the stable embedded depth of a Hall anchor in sand and in rock berm are only 1.7% and 2.7%, respectively. The good agreements indicate that the proposed method is accurate enough to calculate the minimum buried depth of pipeline in rock berm during pipeline design.Accepted versio

A New Ice Quality Prediction Method of Wind Turbine Impeller Based on the Deep Neural Network

More and more wind turbines are installed in cold regions because of better wind resources. In these regions, the high humidity and low temperatures in winter will lead to ice accumulation on the wind turbine impeller. A different icing location or mass will lead to different natural frequency variations of the impeller. In order to monitor the icing situation in time and in advance, a method based on depth neural network technology to predict the icing mass is explored and proposed. Natural-environment icing experiments and iced-impeller modal experiments are carried out, aiming at a 600 W wind turbine, respectively. The mapping relationship between the change rate of the natural frequency of the iced impeller at different icing positions and the icing mass is obtained, and the correlation coefficients are all above 0.93. A deep neural network (DNN) prediction model of ice-coating quality for the impeller was constructed with the change rate of the first six-order natural frequencies as the input factor. The results show that the MAE and MSE of the trained model are close to 0. The average prediction error of the DNN model is 4.79%, 9.35%, 3.62%, 1.63%, respectively, under different icing states of the impeller. It can be seen that the DNN shows the best prediction ability among other methods. The smaller the actual ice-covered mass of the impeller, the larger the relative error of the ice-covered mass predicted by the DNN model. In the same ice-covered state, the relative error will decrease gradually with the increase in ice-covered mass. In a word, using the natural frequency change rate to predict the icing quality is feasible and accurate. The research achievements shown here can provide a new idea for wind farms to realize efficient and intelligent icing monitoring and prediction, provide engineering guidance for the wind turbine blade anti-icing and deicing field, and further reduce the negative impact of icing on wind power generation

A New Ice Quality Prediction Method of Wind Turbine Impeller Based on the Deep Neural Network

More and more wind turbines are installed in cold regions because of better wind resources. In these regions, the high humidity and low temperatures in winter will lead to ice accumulation on the wind turbine impeller. A different icing location or mass will lead to different natural frequency variations of the impeller. In order to monitor the icing situation in time and in advance, a method based on depth neural network technology to predict the icing mass is explored and proposed. Natural-environment icing experiments and iced-impeller modal experiments are carried out, aiming at a 600 W wind turbine, respectively. The mapping relationship between the change rate of the natural frequency of the iced impeller at different icing positions and the icing mass is obtained, and the correlation coefficients are all above 0.93. A deep neural network (DNN) prediction model of ice-coating quality for the impeller was constructed with the change rate of the first six-order natural frequencies as the input factor. The results show that the MAE and MSE of the trained model are close to 0. The average prediction error of the DNN model is 4.79%, 9.35%, 3.62%, 1.63%, respectively, under different icing states of the impeller. It can be seen that the DNN shows the best prediction ability among other methods. The smaller the actual ice-covered mass of the impeller, the larger the relative error of the ice-covered mass predicted by the DNN model. In the same ice-covered state, the relative error will decrease gradually with the increase in ice-covered mass. In a word, using the natural frequency change rate to predict the icing quality is feasible and accurate. The research achievements shown here can provide a new idea for wind farms to realize efficient and intelligent icing monitoring and prediction, provide engineering guidance for the wind turbine blade anti-icing and deicing field, and further reduce the negative impact of icing on wind power generation

Experimental Study on the Noise Evolution of a Horizontal Axis Icing Wind Turbine Based on a Small Microphone Array

In recent years, the global energy mix is shifting towards sustainable energy systems due to the energy crisis and the prominence of ecological climate change. Wind energy resources are abundant in cold regions, and wind turbines are increasingly operating in cold regions with wet natural environments, increasing the risk of wind turbine blade icing. To address the problem of noise source distribution and the frequency characteristic variation of wind turbines in natural icing environments, this paper uses a 112-channel microphone array to acquire the acoustic signals of a horizontal axis wind turbine with a diameter of 2.45m. Using the beamforming technique, the wind turbine noise evolution law characteristics under natural icing environment were studied by field experiments, and the noise source distribution and noise increase in different frequency bands under different icing mass and positions and different angles of attack were analyzed in detail. The results show that under the leading-edge and windward-side icing, the noise source gradually moves toward the blade tip along the spanwise direction with the increase in ice mass. In addition, the total sound pressure level at 460 r/min, 520 r/min, 580 r/min, and 640 r/min are increased by 0.82 dB, 0.85 dB, 0.91 dB, and 0.95 dB, respectively for the leading-edge icing condition in comparison with the uniform icing over the windward side of the blade

Highly Efficient and Exceptionally Durable CO₂ Photoreduction to Methanol over Freestanding Defective Single-Unit-Cell Bismuth Vanadate Layers

Unearthing an ideal model for disclosing the role of defect sites in solar CO₂ reduction remains a great challenge. Here, freestanding gram-scale single-unit-cell <i>o</i>-BiVO₄ layers are successfully synthesized for the first time. Positron annihilation spectrometry and X-ray fluorescence unveil their distinct vanadium vacancy concentrations. Density functional calculations reveal that the introduction of vanadium vacancies brings a new defect level and higher hole concentration near Fermi level, resulting in increased photoabsorption and superior electronic conductivity. The higher surface photovoltage intensity of single-unit-cell <i>o</i>-BiVO₄ layers with rich vanadium vacancies ensures their higher carriers separation efficiency, further confirmed by the increased carriers lifetime from 74.5 to 143.6 ns revealed by time-resolved fluorescence emission decay spectra. As a result, single-unit-cell <i>o</i>-BiVO₄ layers with rich vanadium vacancies exhibit a high methanol formation rate up to 398.3 μmol g^–1 h^–1 and an apparent quantum efficiency of 5.96% at 350 nm, much larger than that of single-unit-cell <i>o</i>-BiVO₄ layers with poor vanadium vacancies, and also the former’s catalytic activity proceeds without deactivation even after 96 h. This highly efficient and spectrally stable CO₂ photoconversion performances hold great promise for practical implementation of solar fuel production