Dispersed Wind Power Planning Method Considering Network Loss Correction with Cold Weather

In order to play a positive role of decentralised wind power on-grid for voltage stability improvement and loss reduction of distribution network,a multi-objective two-stage decentralised wind power planning method is proposed in the paper,which takes into account the network loss correction for the extreme cold region.Firstly,an electro-thermal model is introduced to reflect the effect of temperature on conductor resistance and to correct the results of active network loss calculation;secondly,a two-stage multi-objective two-stage decentralised wind power siting and capacity allocation and reactive voltage optimisation control model is constructed to take account of the network loss correction,and the multi-objective multi-planning model is established in the first stage to consider the whole-life cycle investment cost of WTGs,the system operating cost and the voltage quality of power supply,and the multi-objective planning model is established in the second stage.planning model,and the second stage further develops the reactive voltage control strategy of WTGs on this basis,and obtains the distribution network loss reduction method based on WTG siting and capacity allocation and reactive power control strategy.Finally,the optimal configuration scheme is solved by the manta ray foraging optimisation(MRFO)algorithm,and the loss of each branch line and bus loss of the distribution network before and after the adoption of this loss reduction method is calculated by taking the IEEE33 distribution system as an example,which verifies the practicability and validity of the proposed method,and provides a reference introduction for decision-making for the distributed energy planning of the distribution network.

A generative adversarial network-based unified model integrating bias correction and downscaling for global SST

本文提出了一种基于生成对抗网络的全球海表面温度(sea surface temperature,SST)偏差订正及降尺度整合模型.该模型的生成器使用偏差订正模块将数值模式预测结果进行校正,再用可复用的共享降尺度模块将订正后的数据分辨率逐次提高.该模型的判别器可鉴别偏差订正及降尺度结果的质量,以此为标准进行对抗训练。同时,在对抗损失函数中含有物理引导的动力学惩罚项以提高模型的性能.本研究基于分辨率为1°的GFDL SPEAR模式的SST预测结果,选择遥感系统(Remote Sensing System)的观测资料作为真值,面向月尺度ENSO与IOD事件以及天尺度海洋热浪事件开展了验证试验:模型在将分辨率提高到0.0625°×0.0625°的同时将预测误差减少约90.3%,突破了观测数据分辨率的限制,且与观测结果的结构相似性高达96.46%.

A method for correcting characteristic X-ray net peak count from drifted shadow peak

To correct spectral peak drift and obtain more reliable net counts,this study proposes a long short-term memory(LSTM)model fused with a convolutional neural network(CNN)to accurately estimate the relevant parameters of a nuclear pulse signal by learning of samples.A predefined mathematical model was used to train the CNN-LSTM model and generate a dataset composed of distorted pulse sequences.The trained model was validated using simulated pulses.The relative errors in the amplitude estimation of pulse sequences with different degrees of distortion were obtained using triangular shaping,CNN-LSTM,and LSTM models.As a result,for severely distorted pulses,the relative error of the CNN-LSTM model in estimating the pulse parameters was reduced by 14.35%compared with that of the triangular shaping algorithm.For slightly distorted pulses,the relative error of the CNN-LSTM model was reduced by 0.33%compared with that of the triangular shaping algorithm.The model was then evaluated considering two performance indicators,the correction ratio and the efficiency ratio,which represent the proportion of the increase in peak area of the two characteristic peak regions of interest(ROIs)to the peak area of the corrected characteristic peak ROI and the proportion of the increase in peak area of the two characteristic peak ROIs to the peak areas of the two shadow peak ROI,respectively.Ten measurement results of the iron ore samples indicate that approximately 86.27%of the decreased peak area of the shadow peak ROI was corrected to the characteristic peak ROI,and the proportion of the corrected peak area to the peak area of the characteristic peak ROI was approximately 1.72%.The proposed CNN-LSTM model can be applied to X-ray energy spectrum correction,which is of great significance for X-ray spectroscopy and elemental content analyses.

Self-correcting wavelet neural network control of continuous rotary electro-hydraulic servo motor

In allusion to the problem of friction,leakage,vibration and noise existing in continuous rotary motor electro-hydraulic servo system,highly nonlinearity and uncertainties affecting the system performance,based on the transfer function of electro-hydraulic servo system,a kind of Pol-Ind friction model is proposed.The parameters of Pol-Ind friction model are identified and the accurate mathematical model of friction torque is obtained by experiment.The self-correcting wavelet neural network(WNN)controller is proposed,and Adam optimization algorithm is used to perform gradient optimization on scale factor and displacement factor in wavelet basis function,so as to improve the speed and precision of parameter optimization.Through comparative simulation analysis,it is clearly that the self-correcting WNN controller can effectively improve the frequency response and tracking accuracy of continuous rotary motor electro-hydraulic servo system.

Recurrent neural network decoding of rotated surface codes based on distributed strategy

Quantum error correction is a crucial technology for realizing quantum computers.These computers achieve faulttolerant quantum computing by detecting and correcting errors using decoding algorithms.Quantum error correction using neural network-based machine learning methods is a promising approach that is adapted to physical systems without the need to build noise models.In this paper,we use a distributed decoding strategy,which effectively alleviates the problem of exponential growth of the training set required for neural networks as the code distance of quantum error-correcting codes increases.Our decoding algorithm is based on renormalization group decoding and recurrent neural network decoder.The recurrent neural network is trained through the ResNet architecture to improve its decoding accuracy.Then we test the decoding performance of our distributed strategy decoder,recurrent neural network decoder,and the classic minimum weight perfect matching(MWPM)decoder for rotated surface codes with different code distances under the circuit noise model,the thresholds of these three decoders are about 0.0052,0.0051,and 0.0049,respectively.Our results demonstrate that the distributed strategy decoder outperforms the other two decoders,achieving approximately a 5%improvement in decoding efficiency compared to the MWPM decoder and approximately a 2%improvement compared to the recurrent neural network decoder.

Stability Study of Low Voltage Electrical Distribution Network: Audit and Improvement of DJEGBE Mini Solar Photovoltaic Power Plant in the Commune of OUESSE (Benin)

The supply of quality energy is a major concern for distribution network managers. This is the case for the company ASEMI, whose subscribers on the DJEGBE mini-power station network are faced with problems of current instability, voltage drops, and repetitive outages. This work is part of the search for the stability of the electrical distribution network by focusing on the audit of the DJEGBE mini photovoltaic solar power plant electrical network in the commune of OUESSE (Benin). This aims to highlight malfunctions on the low-voltage network to propose solutions for improving current stability among subscribers. Irregularities were noted, notably the overloading of certain lines of the PV network, implying poor distribution of loads by phase, which is the main cause of voltage drops;repetitive outages linked to overvoltage caused by lightning and overcurrent due to overload;faulty meters, absence of earth connection at subscribers. Peaks in consumption were obtained at night, which shows that consumption is greater in the evening. We examined the existing situation and processed the data collected, then simulated the energy consumption profiles with the network analyzer “LANGLOIS 6830” and “Excel”. The power factor value recorded is an average of 1, and the minimum value is 0.85. The daily output is 131.08 kWh, for a daily demand of 120 kWh and the average daily consumption is 109.92 kWh, or 83.86% of the energy produced per day. These results showed that the dysfunctions are linked to the distribution and the use of produced energy. Finally, we proposed possible solutions for improving the electrical distribution network. Thus, measures without investment and those requiring investment have been proposed.

Neural-Network-Based Charge Density Quantum Correction of Nanoscale MOSFETs

For the treatment of the quantum effect of charge distribution in nanoscale MOSFETs,a quantum correction model using Levenberg-Marquardt back-propagation neural networks is presented that can predict the quantum density from the classical density. The training speed and accuracy of neural networks with different hidden layers and numbers of neurons are studied. We conclude that high training speed and accuracy can be obtained using neural networks with two hidden layers,but the number of neurons in the hidden layers does not have a noticeable effect, For single and double-gate nanoscale MOSFETs, our model can easily predict the quantum charge density in the silicon layer,and it agrees closely with the Schrodinger-Poisson approach.

Color Correction for Multi-view Video Using Energy Minimization of View Networks

Systems using numerous cameras are emerging in many fields due to their ease of production and reduced cost, and one of the fields where they are expected to be used more actively in the near future is in image-based rendering （IBR）. Color correction between views is necessary to use multi-view systems in IBR to make audiences feel comfortable when views are switched or when a free viewpoint video is displayed. Color correction usually involves two steps： the first is to adjust camera parameters such as gain, brightness, and aperture before capture, and the second is to modify captured videos through image processing. This paper deals with the latter, which does not need a color pattern board. The proposed method uses scale invariant feature transform （SIFT） to detect correspondences, treats RGB channels independently, calculates lookup tables with an energy-minimization approach, and corrects captured video with these tables. The experimental results reveal that this approach works well.

Determination of quantum toric error correction code threshold using convolutional neural network decoders

Quantum error correction technology is an important solution to solve the noise interference generated during the operation of quantum computers.In order to find the best syndrome of the stabilizer code in quantum error correction,we need to find a fast and close to the optimal threshold decoder.In this work,we build a convolutional neural network(CNN)decoder to correct errors in the toric code based on the system research of machine learning.We analyze and optimize various conditions that affect CNN,and use the RestNet network architecture to reduce the running time.It is shortened by 30%-40%,and we finally design an optimized algorithm for CNN decoder.In this way,the threshold accuracy of the neural network decoder is made to reach 10.8%,which is closer to the optimal threshold of about 11%.The previous threshold of 8.9%-10.3%has been slightly improved,and there is no need to verify the basic noise.

Nonlinear Correction of Pressure Sensor Based on Depth Neural Network

With the global climate change,the high-altitude detection is more and more important in the climate prediction,and the input-output characteristic curve of the air pressure sensor is offset due to the interference of the tested object and the environment under test,and the nonlinear error is generated.Aiming at the difficulty of nonlinear correction of pressure sensor and the low accuracy of correction results,depth neural network model was established based on wavelet function,and Levenberg-Marquardt algorithm is used to update network parameters to realize the nonlinear correction of pressure sensor.The experimental results show that compared with the traditional neural network model,the improved depth neural network not only accelerates the convergence rate,but also improves the correction accuracy,meets the error requirements of upper-air detection,and has a good generalization ability,which can be extended to the nonlinear correction of similar sensors.

An Improved Scene-based Nonuniformity Correction Algorithm for Infrared Focal Plane Arrays Using Neural Networks

The improved scene-based adaptive nonuniformity correction (NUC) algorithms using a neural network (NNT) approach for infrared image sequences are presented and analyzed. The retina-like neural networks using steepest descent model was the first proposed infrared focal plane arrays (IRFPA) nonuniformity compensation method,which can perform parameter estimation of the sensors over time on a frame by frame basis. To increase the strength and the robustness of the NNT algorithm and to avoid the presence of ghosting artifacts,some optimization techniques,including momentum term,regularization factor and adaptive learning rate,were executed in the parameter learning process. In this paper,the local median filtering result of AX^U_ ij (n) is proposed as an alternative value of desired network output of neuron X_ ij (n),denoted as T_ ij (n),which is the local spatial average of AX^U_ ij (n) in traditional NNT methods. Noticeably,the NUC algorithm is inter-frame adaptive in nature and does not rely on any statistical assumptions on the scene data in the image sequence. Applications of this algorithm to the simulated video sequences and real infrared data taken with PV320 show that the correction results of image sequence are better than that of using original NNT approach,especially for the short-time image sequences (several hundred frames) subjected to the dense impulse noises with a number of dead or saturated pixels.

Adaptive nonuniformity correction for IRFPA sensors based on neural network framework

For infrared focal plane graded during signal acquisition array sensors, imagery is departicularly nonuniformity. In this paper, an adaptive nonuniformity correction technique is proposed which simultaneously estimates detector-level and readout- channel-level correction parameters using neural network approaches. Firstly, an improved neural network framework is designed to compute the desired output. Secondly, an adaptive learning rate rule is used in the gain and offset parameter estimation process. Experimental results show the proposed algorithm can achieve a faster convergence speed and better stability, remove nonuniformity and track parameters drift effectively, and present a good adaptability to scene changes and nonuniformity conditions.

A Data Driven Security Correction Method for Power Systems with UPFC

The access of unified power flow controllers(UPFC)has changed the structure and operation mode of power grids all across the world,and it has brought severe challenges to the traditional real-time calculation of security correction based on traditionalmodels.Considering the limitation of computational efficiency regarding complex,physical models,a data-driven power system security correction method with UPFC is,in this paper,proposed.Based on the complex mapping relationship between the operation state data and the security correction strategy,a two-stage deep neural network(DNN)learning framework is proposed,which divides the offline training task of security correction into two stages:in the first stage,the stacked auto-encoder(SAE)classification model is established,and the node correction state(0/1)output based on the fault information;in the second stage,the DNN learningmodel is established,and the correction amount of each action node is obtained based on the action nodes output in the previous stage.In this paper,the UPFC demonstration project of NanjingWest Ring Network is taken as a case study to validate the proposed method.The results show that the proposed method can fully meet the real-time security correction time requirements of power grids,and avoid the inherent defects of the traditional model method without an iterative solution and can also provide reasonable security correction strategies for N-1 and N-2 faults.

Text-to-Sketch Synthesis via Adversarial Network

In the past,sketches were a standard technique used for recognizing offenders and have remained a valuable tool for law enforcement and social security purposes.However,relying on eyewitness observations can lead to discrepancies in the depictions of the sketch,depending on the experience and skills of the sketch artist.With the emergence of modern technologies such as Generative Adversarial Networks(GANs),generating images using verbal and textual cues is now possible,resulting in more accurate sketch depictions.In this study,we propose an adversarial network that generates human facial sketches using such cues provided by an observer.Additionally,we have introduced an Inverse Gamma Correction Technique to improve the training and enhance the quality of the generated sketches.To evaluate the effectiveness of our proposed method,we conducted experiments and analyzed the results using the inception score and Frechet Inception Distance metrics.Our proposed method achieved an overall inception score of 1.438±0.049 and a Frechet Inception Distance of 65.29,outperforming other state-of-the-art techniques.

大功率拖拉机多工况换挡自适应控制策略

拖拉机牵引不同农机具完成不同作业,其作业要求随着牵引机具的不同而变化。针对大功率拖拉机在不同工况下作业要求不同的问题,提出一种多工况换挡自适应控制方法。分析不同工况对拖拉机动力性经济性的要求,以滑转率、油门开度和速度为参数,根据工况要求计算兼顾动力性和经济性的理论换挡规律,采用神经网络离线训练换挡规律实现挡位智能控制;针对重载荷下随机载荷波动导致循环换挡问题,引入加速度和油门开度变化量作为参数,利用模糊逻辑判断拖拉机负载和驾驶员操作意图得到速度修正系数,对换挡速度进行修正,扩大挡位使用范围;通过对拖拉机纵向动力学分析,利用Simulink搭建大功率拖拉机数学仿真模型,并建立变速箱换挡控制系统硬件在环仿真平台验证换挡策略的有效性。仿真结果表明,在燃油经济性方面,道路运输和轻载荷作业工况燃油经济性分别下降5.78%、3.28%。在动力性方面,保证克服牵引阻力的同时,轻载荷和重载荷工况加速时间较快,速度波动减小且有效避免重载荷工况下循环换挡问题。

基于PSO-BP神经网络的磨机传动系统模型修正

针对磨机传动系统结构的复杂性、部件间约束条件的不确定性以及非线性等因素,提出了一种基于PSO-BP神经网络的有限元模型修正方法。通过改进BP神经网络逼近设计参数和特征量间的非线性映射关系,结合实际结构响应,利用神经网络的泛化特性,得到了模型设计参数值。修正后频率误差从最高18%降到4%左右,修正系数误差范围均在0.5%以内,明显提高了有限元模型精度;同时,又不需要大量迭代求解步骤,避开了传统反问题模型修正法的复杂非线性优化过程,提升了效率,验证了PSO-BP神经网络法应用于大型磨机传动系统上的可行性,为后续传动系统整体分析奠定了基础。

基于BP神经网络的重力仪高机动状态快速调平修正技术

针对平台式重力仪大机动状态后测量能力恢复慢的问题,提出一种基于BP神经网络的重力仪稳定平台快速调平修正技术。首先,针对动态重力测量在测量平台大机动状态后调平能力不足的问题,研究了基于BP神经网络的平台姿态高效、准确解算方法;其次,利用惯性元件和卫星导航系统(GNSS)的信息优化BP神经网络,形成不同条件的平台姿态提取优化模型;最后,利用模拟仿真实验和实际机载动态重力测量数据验证所提方法的有效性和准确性。实验结果表明在大机动的动态条件下采用所提方法可以扶正重力仪稳定平台,将机动后重力仪稳定平台稳定时间缩短83.3%以上,提升动态重力测量效率。

基于改进DBNet和SVTR算法的连铸板坯号检测与识别

针对钢铁连铸产线板坯号识别字符区域小、光照变化复杂、板坯号图像质量差等问题,提出了一种基于深度学习的连铸板坯号检测与识别两阶段算法。首先,基于采集的连铸产线板坯图像,制备用于板坯号检测与识别的数据集;其次,在板坯号检测阶段,基于DBNet算法设计一种AD-PAN特征融合结构,以增强检测算法的多尺度特征融合能力和扩大感受野,提高板坯号定位精度;再次,在板坯号识别阶段,引入SPIN矫正网络和SVTR板坯号识别网络进行端到端训练,使其能够主动转换输入亮度,并改善字符间以及字符与背景间色彩失真的问题。最后,在自制的板坯号检测与识别数据集上进行了对比实验。实验结果表明,本研究提出的算法能够有效定位辊道上不同位置的板坯,并且在复杂背景下对板坯号进行鲁棒识别。其中,板坯号检测Hmean数值为97.92%,板坯号识别的准确率为97.33%,验证了本文所提算法具有较高的板坯号检测与识别精度。

可见光通信网络信号非线性失真自适应校正方法研究

可见光通信网络是现今通信领域中的主要构成部分之一,但其应用的物理设备(LED照明设备)具有非线性特征,致使可见光通信网络信号出现非线性失真情况,影响着用户通信的质量,故提出可见光通信网络信号非线性失真自适应校正方法研究。通过深入分析可知信号非线性失真分为非线性转换失真和非线性限幅失真,以此为基础,设计信号非线性失真校正电路(DG预失真校正电路与DP预失真校正电路),引入自适应算法制定信号非线性失真自适应校正架构,确定信号非线性失真自适应校正公式,从而实现了信号非线性失真的有效校正。实验数据显示:应用提出方法获得的信号非线性失真自适应校正结果中不存在非线性失真现象,通信信号接收误码率最小值为3%,充分证实了提出方法应用性能更加优质。

基于低风速功率修正和损失函数改进的超短期风电功率预测

风电功率具有较强的波动性和随机性。为进一步提升风电功率的预测精度,提出一种基于低风速功率修正和损失函数改进的超短期风电功率预测模型。该模型采用卷积神经网络、自注意力机制和双向门控循环单元捕获风电功率序列的长期时序依赖关系。为了解决低风速下待风状态神经网络难以精确拟合的问题,模型通过预测风速并结合当前时段的风电功率对低风速段的预测功率进行修正。针对参数训练的稳定性问题,模型通过改进预测策略和共享权重,引入一种多元非线性的损失函数来提取序列间的关联性。结果表明,所提模型在多项误差指标中均优于对比模型,能够有效提升超短期风电功率的预测效果。