Analogue correction method of errors and its application to numerical weather prediction

In this paper, an analogue correction method of errors （ACE） based on a complicated atmospheric model is further developed and applied to numerical weather prediction （NWP）. The analysis shows that the ACE can effectively reduce model errors by combining the statistical analogue method with the dynamical model together in order that the information of plenty of historical data is utilized in the current complicated NWP model, Furthermore, in the ACE, the differences of the similarities between different historical analogues and the current initial state are considered as the weights for estimating model errors. The results of daily, decad and monthly prediction experiments on a complicated T63 atmospheric model show that the performance of the ACE by correcting model errors based on the estimation of the errors of 4 historical analogue predictions is not only better than that of the scheme of only introducing the correction of the errors of every single analogue prediction, but is also better than that of the T63 model.

基于Involution Prediction Head的小目标检测算法

针对通用目标检测算法在检测小目标时存在错检和漏检等问题,提出了一种小目标检测算法IPH(Involution Prediction Head),将其运用在YOLOv4和YOLOv5的检测头部分,在VOC2007数据集上的实验结果表明,运用IPH后的YOLOv4小目标检测精度APs(AP for small objects)相比原始算法提升了1.1%,在YOLOv5上的APs更是提升了5.9%。经智能交通检测数据集进一步检验,IPH算法和去下采样能有效提升小目标检测精度,减少错检和漏检的情况。

Analogue-Dynamical Prediction of Monsoon Precipitation in Northeast China Based on Dynamic and Optimal Configuration of Multiple Predictors

Based on the National Climate Center （NCC） of China operational seasonal prediction model results for the period 1983-2009 and the US National Weather Service Climate Prediction Center merged analysis of precipitation in the same period, together with the 74 circulation indices of NCC Climate System Diagnostic Division and 40 climate indices of NOAA of US during 1951 2009, an analogue-dynamical technique for objective and quantitative prediction of monsoon precipitation in Northeast China is proposed and implemented. Useful information is extracted from the historical data to estimate the model forecast errors. Dominant predictors and the predictors that exhibit evolving analogues are identified through cross validating the anomaly correlation coefficients （ACC） among single predictors, meanwhile with reference of the results from the dynamic analogue bias correction using four analogue samples. Next, an optimal configuration of multiple predictors is set up and compared with historical optimal multi-predictor configurations and then dynamically adjusted. Finally, the model errors are evaluated and utilized to correct the NCC operational seasonal prediction model results, and the forecast of monsoon precipitation is obtained at last. The independent sample validation shows that this technique has effectively improved the monsoon precipitation prediction skill during 2005 -2009. This study demonstrates that the analogue-dynamical approach is feasible in operational prediction of monsoon precipitation.

Multi-Scheme Corrected Dynamic–Analogue Prediction of Summer Precipitation in Northeastern China Based on BCC_CSM

Based on summer precipitation hindcasts for 1991-2013 produced by the Beijing Climate Center Climate System Model （BCC_CSM）, the relationship between precipitation prediction error in northeastern China （NEC） and global sea surface temperature is analyzed, and dynamic-analogue prediction is carried out to improve the summer precipitation prediction skill of BCC_CSM, through taking care of model historical analogue prediction error in the real-time output. Seven correction schemes such as the systematic bias correction, pure statistical correction, dynamic-analogue correction, and so on, are designed and compared. Independent hindcast results show that the 5-yr average anomaly correlation coefficient （ACC） of summer precipitation is respectively improved from -0. 13/0.15 to 0.16/0.24 for 2009-13/1991-95 when using the equally weighted dynamic-analogue correction in the BCC_CSM prediction, which takes the arithmetical mean of the correction based on regional average error and that on grid point error. In addition, probabilistic prediction using the results from the multiple correction schemes is also performed and it leads to further improved 5-yr average prediction accuracy.

Pedestrian wind flow prediction using spatial-frequency generative adversarial network

Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban conditions.Predicting pedestrian wind flow during the early design stages is essential but currently suffers from inefficiencies in numerical simulations.Deep learning,particularly generative adversarial networks(GAN),has been increasingly adopted as an alternative method to provide efficient prediction of pedestrian wind flow.However,existing GAN-based wind flow prediction schemes have limitations due to the lack of considering the spatial and frequency characteristics of wind flow images.This study proposes a novel approach termed SFGAN,which embeds spatial and frequency characteristics to enhance pedestrian wind flow prediction.In the spatial domain,Gaussian blur is employed to decompose wind flow into components containing wind speed and distinguished flow edges,which are used as the embedded spatial characteristics.Detailed information of wind flow is obtained through discrete wavelet transformation and used as the embedded frequency characteristics.These spatial and frequency characteristics of wind flow are jointly utilized to enforce consistency between the predicted wind flow and ground truth during the training phase,thereby leading to enhanced predictions.Experimental results demonstrate that SFGAN clearly improves wind flow prediction,reducing Wind_MAE,Wind_RMSE and the Fréchet Inception Distance(FID)score by 5.35%,6.52%and 12.30%,compared to the previous best method,respectively.We also analyze the effectiveness of incorporating the spatial and frequency characteristics of wind flow in predicting pedestrian wind flow.SFGAN reduces errors in predicting wind flow at large error intervals and performs well in wake regions and regions surrounding buildings.The enhanced predictions provide a better understanding of performance variability,bringing insights at the early design stage to improve pedestrian wind comfort.The proposed spatial-frequency loss term is general and can be flexibly integrated with other generative models to enhance performance with only a slight computational cost.

Strategy and methodology of dynamical analogue prediction

In order to effectively improve numerical prediction level by using current models and data, the strategy and methodology of dynamical analogue prediction (DAP) is deeply studied in the present paper. A new idea to predict the prediction errors of dynamical model on the basis of historical analogue information is put forward so as to transform the dynamical prediction problem into the estimation problem of prediction errors. In terms of such an idea, a new prediction method of final analogue correction of errors (FACE) is developed. Furthermore, the FACE is applied to extra-seasonal prediction experiments on an operational atmosphere-ocean coupled general circulation model. Prediction results of summer mean circulation and total precipitation show that the FACE can to some extent reduce prediction errors, recover prediction variances, and improve prediction skills. Besides, sensitive experiments also show that predictions based on the FACE are evidently influenced by the number of analogues, analogue-selected variables and analogy metric.

Prediction of the ^(13)C NMR Chemical Shifts of Stilbene Analogues by GIAO Method

After the geometry optimization at B3LYP/6-31＋G（d,p） level,the calculations of the NMR chemical shifts of a series of stilbene analogues were carried out by means of Gauge Including Atomic Orbitals（GIAO） method at HF/6-31＋G（d） level and B3LYP/6-311G＋（2d,p） level,respectively.The 13C NMR chemical shifts calculated at both HF/6-31＋G（d） and B3LYP/6-31＋G（d,p） levels are in agreement with the observed values.By virtue of a series of linear correction equations（δpred.=a＋bδcalcd.） of the 13C chemical shifts,accurate prediction of 13C chemical shifts was achieved for the new stilbene compounds.For the 13C NMR chemical shifts calculated at HF/6-31＋G（d） level,the linear correlation of δpred.with δexptl.is excellent,and the square of correlation coefficient,r2,is 0.9985.The maximum absolute difference between δpred.and δexptl.,Δδ,is 2.3,and the root-mean-square error between δpred.and δexptl.is 0.98.In the meantime,for those obtained at B3LYP/6-31＋G（d,p） level,the linear correlation of δpred.with δexptl.is also excellent,and the square of correlation coefficient,r2,is up to 0.9987.The maximum absolute difference between δpred.and δexptl.,Δδ,is 2.2,and the root-mean-square error between δpred.and δexptl.is only 0.88.

Prediction of the ^(13)C NMR Chemical Shifts of Fluorenone Analogues by the GIAO Method

After the geometry optimization at B3LYP/6-31＋G（d,p） level,the NMR calcula-tions of a series of fluorenone analogues have been carried out by GIAO method at HF/6-31＋G（d） level and B3LYP/6-311G＋（2d,p） level,respectively.The 13C NMR chemical shifts calculated at HF/6-31＋G（d） level show better agreement with the observed values.By a series of linear correction equations （δpred=a ＋ bδcalc）,accurate prediction of 13C chemical shifts was achieved for the new fluorenone compound.The linear correlation of δpred with δexptl is excellent,and the square of correlation coefficient,r2,is up to 0.994.The maximum absolute difference between δpred and δexptl,Δδ,is 4.6 ppm,and the root-mean-square error between δpred and δexptl is only 2.6 ppm.

Multi-objective interval prediction of wind power based on conditional copula function

Interval prediction of wind power,which features the upper and lower limits of wind power at a given confidence level,plays a significant role in accurate prediction and stability of the power grid integrated with wind power.However,the conventional methods of interval prediction are commonly based on a hypothetic probability distribution function,which neglects the correlations among various variables,leading to the decrease of prediction accuracy.Therefore,we improve the multi-objective interval prediction based on the conditional copula function,through which we can fully utilize the correlations among variables to improve prediction accuracy without an assumed probability distribution function.We use the multi-objective optimization method of nondominated sorting genetic algorithm-II(NSGA-II)to obtain the optimal solution set.The particular best solution is weighted by the prediction interval average width(PIAW)and prediction interval coverage probability(PICP)to pick the optimized solution in practical examples.Finally,we apply the proposed method to three wind power plants in different cities in China as examples forvalidation and obtain higher prediction accuracy compared with other methods,i.e.,relevance vector machine(RVM),artificial neural network(ANN),and particle swarm optimization kernel extreme learning machine(PSO-KELM).These results demonstrate the superiority and practicability of this method in interval prediction of wind power.

Separation density prediction of geldart A^(-)dense medium in gas-solid fluidized bed coal beneficiators

Gas-solid Fluidized Bed Coal Beneficiator(GFBCB)process is a crucial dry coal beneficiation fluidization technology.The work employs the GFBCB process alongside a novel Geldart A^(-)dense medium,consisting of Geldart A magnetite particles and Geldart C ultrafine coal,to separate small-size separated objects in the GFBCB.The effects of various operational conditions,including the volume fraction of ultrafine coal,the gas velocity,the separated objects size,and the separation time,were investigated on the GFBCB's separation performance.The results indicated that the probable error for 6∼3 mm separated objects could be controlled within 0.10 g/cm^(3).Compared to the traditional Geldart B/D dense medium,the Geldart A/A^(-)dense medium exhibited better size-dependent separation performance with an overall probable error 0.04∼0.12 g/cm^(3).Moreover,it achieved a similar separation accuracy to the Geldart B/D dense medium fluidized bed with different external energy for the small-size object beneficiation.The work furthermore validated a separation density prediction model based on theoretical derivation,available for both Geldart B/D dense medium and Geldart A/A^(-)dense medium at different operational conditions.

Prediction of the ^(13)C NMR Chemical Shifts of 9,10-Dihydrophenanthrene Analogues by the GIAO Method

After the geometry optimizations at the B3LYP/6-31＋G（d,p） level, the NMR calculations of a series of 9,10-dihydrophenanthrene analogues have been carried out by GIAO method at the HF/6-31＋G（d） level. The calculated ^13C NMR chemical shifts are in agreement with the observed values. By a series of linear correlation equations （δpred = a ＋ bδcal.c） of the ^13C chemical shifts, accurate prediction of ^13C chemical shifts was achieved for the new 9,10- dihydrophenanthrene compound, for which the predicted ^13C NMR chemical shifts are in quite good agreement with the experimental values. The linear correlation between δpred and δexptl is excellent, and the square of correlation coefficient, r^2, is up to 0.9973. The maximum absolute difference between δpred and δexptl, △δ, is 4.5 ppm, and the rms error between δpred and δexpt is 2.55 ppm. In the meantime, according to the theoretical predicted result, we could confirm that the new 9,10-dihydrophenanthrene analogue is erianthridin （2,7-dihydroxy-3,4-dimethoxy-9,10-dihydro- phenanthrene）.

A drifting trajectory prediction model based on object shape and stochastic mo-tion features

There is a huge demand to develop a method for marine search and rescue（SAR） operators automatically predicting the most probable searching area of the drifting object. This paper presents a novel drifting prediction model to improve the accuracy of the drifting trajectory computation of the sea-surface objects. First, a new drifting kinetic model based on the geometry characteristics of the objects is proposed that involves the effects of the object shape and stochastic motion features in addition to the traditional factors of wind and currents. Then, a computer simulation-based method is employed to analyze the stochastic motion features of the drifting objects, which is applied to estimate the uncertainty parameters of the stochastic factors of the drifting objects. Finally, the accuracy of the model is evaluated by comparison with the flume experimental results. It is shown that the proposed method can be used for various shape objects in the drifting trajectory prediction and the maritime search and rescue decision-making system.

Comparative evaluation of three machine learning algorithms on improving orbit prediction accuracy

In this paper,the recently developed machine learning(ML)approach to improve orbit prediction accuracy is systematically investigated using three ML algorithms,including support vector machine(SVM),artificial neural network(ANN),and Gaussian processes(GPs).In a simulation environment consisting of orbit propagation,measurement,estimation,and prediction processes,totally 12 resident space objects(RSOs)in solar-synchronous orbit(SSO),low Earth orbit(LEO),and medium Earth orbit(MEO)are simulated to compare the performance of three ML algorithms.The results in this paper show that ANN usually has the best approximation capability but is easiest to overfit data;SVM is the least likely to overfit but the performance usually cannot surpass ANN and GPs.Additionally,the ML approach with all the three algorithms is observed to be robust with respect to the measurement noise.

Modification to first-order Born approximation for improved prediction of scattered sound from weakly scattering objects

When deriving the Fourier diffraction theorem based on the first-order Born approximation,the difference between wave number of the scattering object and that of the surrounding medium is ignored,causing substantial errors in sound scattering prediction.This paper modifies the Born approximation by taking into account the amplitude and phase changes between the scattering object and the water due to the wave number difference.By changing the radius and center position of the sampling circle in the Fourier domain,accuracy of the predicted sound scattering is improved.With the modified Born approximation,the computed far-field directional pattern of the scattered sound from a circular cylinder is in good agreement with the rigorous solution.Numerical calculations for several objects with different shapes are used to show applicability and effectiveness of the proposed method.

3D Path prediction of moving objects in a video-augmented indoor virtual environment

Augmented virtual environments(AVE)combine real-time videos with 3D scenes in a Digital Earth System or 3D GIS to present dynamic information and a virtual scene simultaneously.AVE can provide solutions for continuous tracking of moving objects,camera scheduling,and path planning in the real world.This paper proposes a novel approach for 3D path prediction of moving objects in a video-augmented indoor virtual environment.The study includes 3D motion analysis of moving objects,multi-path prediction,hierarchical visualization,and path-based multi-camera scheduling.The results show that these methods can give a closed-loop process of 3D path prediction and continuous tracking of moving objects in an AVE.The path analysis algorithms proved accurate and time-efficient,costing less than 1.3 ms to get the optimal path.The experiment ran a 3D scene containing 295,000 triangles at around 35 frames per second on a laptop with 1 GB of graphics card memory,which means the performance of the proposed methods is good enough to maintain high rendering efficiency for a video-augmented indoor virtual scene.

Joint salient object detection and existence prediction

Recent advances in supervised salient object detection modeling has resulted in significant performance improvements on benchmark datasets. However, most of the existing salient object detection models assume that at least one salient object exists in the input image. Such an assumption often leads to less appealing saliency maps on the background images with no salient object at all. Therefore, handling those cases can reduce the false positive rate of a model. In this paper, we propose a supervised learning approach for jointly addressing the salient object detection and existence prediction problems. Given a set of background-only images and images with salient objects, as well as their salient object annotations, we adopt the structural SVM framework and formulate the two problems jointly in a single integrated objective function: saliency labels of superpixels are involved in a classification term conditioned on the salient object existence variable, which in turn depends on both global image and regional saliency features and saliency labels assignments. The loss function also considers both image-level and regionlevel mis-classifications. Extensive evaluation on benchmark datasets validate the effectiveness of our proposed joint approach compared to the baseline and state-of-the-art models.

Theoretical Basis and Application of an Analogue-Dynamical Model in the Lorenz System

The theoretical basis and application of an analogue-dynamical model （ADM） in the Lorenz system is studied. The ADM can effectively combine statistical and dynamical methods in which the small disturbance of the current initial value superimposed on the historical analogue reference state can be regarded as a prediction objective. Primary analyses show that under the condition of appending disturbances in model parameters, the model errors of ADM are much smaller than those of the pure dynamical model （PDM）. The characteristics of predictability on the ADM in the Lorenz system are analyzed in phase space by conducting case studies and global experiments. The results show that the ADM can quite effectively reduce prediction errors and prolong the valid time of the prediction in most situations in contrast to the PDM, but when model errors are considerably small, the latter will be superior to the former. To overcome such a problem, the multi-reference-state updating can be applied to introduce the information of multi-analogue and update analogue and can exhibit exciting performance in the ADM.

Extracting predictable components and forecasting techniques in extended-range numerical weather prediction

This paper refers to the CNOP-related algorithms and formulates the practical method and forecast techniques of extracting predictable components in a numerical model for predictable components on extended-range scales.Model variables are divided into predictable components and unpredictable chaotic components from the angle of model prediction error growth.The predictable components are defined as those with a slow error growth at a given range.A targeted numerical model for predictable components is established based on the operational dynamical extended-range forecast(DERF)model of the National Climate Center.At the same time,useful information in historical data are combined to find the fields for predictable components in the numerical model that are similar to those for the predictable components in historical data,reducing the variable dimensions in a similar judgment process and further correcting prediction errors of predictable components.Historical data is used to obtain the expected value and variance of the chaotic components through the ensemble forecast method.The numerical experiment results show that this method can effectively improve the forecast skill of the atmospheric circulation field in the 10–30 days extended-range numerical model and has good prospects for operational applications.

基于计算机视觉的电力作业人员行为分析研究现状与展望

电力作业人员的有效监管是保障电力安全生产的基础。该文对电力视频中作业人员的行为识别研究进行了归类总结,涵盖静态行为分析(穿戴分析、动作分析和组合分析)和动态行为分析(复杂动作、时序行为和行为预测等);详细综述了电力作业行为分析中的核心算法模块,包括目标检测、姿态估计和视频跟踪等;论述了电力作业行为识别在算法高效性、鲁棒性、灵活性等方面所面临的应用难点和挑战,并展望了电力作业行为智能监控领域的未来发展方向,特别强调了在软硬件结合、通用大模型、生成式人工智能方面进行技术创新和改进所蕴含的潜在机会。