Structural plane recognition from three-dimensional laser scanning points using an improved region-growing algorithm based on the robust randomized Hough transform

The staggered distribution of joints and fissures in space constitutes the weak part of any rock mass.The identification of rock mass structural planes and the extraction of characteristic parameters are the basis of rock-mass integrity evaluation,which is very important for analysis of slope stability.The laser scanning technique can be used to acquire the coordinate information pertaining to each point of the structural plane,but large amount of point cloud data,uneven density distribution,and noise point interference make the identification efficiency and accuracy of different types of structural planes limited by point cloud data analysis technology.A new point cloud identification and segmentation algorithm for rock mass structural surfaces is proposed.Based on the distribution states of the original point cloud in different neighborhoods in space,the point clouds are characterized by multi-dimensional eigenvalues and calculated by the robust randomized Hough transform(RRHT).The normal vector difference and the final eigenvalue are proposed for characteristic distinction,and the identification of rock mass structural surfaces is completed through regional growth,which strengthens the difference expression of point clouds.In addition,nearest Voxel downsampling is also introduced in the RRHT calculation,which further reduces the number of sources of neighborhood noises,thereby improving the accuracy and stability of the calculation.The advantages of the method have been verified by laboratory models.The results showed that the proposed method can better achieve the segmentation and statistics of structural planes with interfaces and sharp boundaries.The method works well in the identification of joints,fissures,and other structural planes on Mangshezhai slope in the Three Gorges Reservoir area,China.It can provide a stable and effective technique for the identification and segmentation of rock mass structural planes,which is beneficial in engineering practice.

Multi-Modal Medical Image Fusion Based on Improved Parameter Adaptive PCNN and Latent Low-Rank Representation

Multimodal medical image fusion can help physicians provide more accurate treatment plans for patients, as unimodal images provide limited valid information. To address the insufficient ability of traditional medical image fusion solutions to protect image details and significant information, a new multimodality medical image fusion method(NSST-PAPCNNLatLRR) is proposed in this paper. Firstly, the high and low-frequency sub-band coefficients are obtained by decomposing the source image using NSST. Then, the latent low-rank representation algorithm is used to process the low-frequency sub-band coefficients;An improved PAPCNN algorithm is also proposed for the fusion of high-frequency sub-band coefficients. The improved PAPCNN model was based on the automatic setting of the parameters, and the optimal method was configured for the time decay factor αe. The experimental results show that, in comparison with the five mainstream fusion algorithms, the new algorithm has significantly improved the visual effect over the comparison algorithm,enhanced the ability to characterize important information in images, and further improved the ability to protect the detailed information;the new algorithm has achieved at least four firsts in six objective indexes.

An image encryption algorithm based on improved baker transformation and chaotic S-box

The algorithm is an image encryption algorithm based on the improved baker transformation and chaotic substitution box(S-box). It mainly uses the initial values and parameters of a one-dimensional logistic chaotic system as an encryption key. Specifically, in the image scrambling stage, the algorithm primarily uses an improved baker transform method to process the image. In the image diffusion stage, the algorithm first uses the chaotic S-box method to process the encryption key. Secondly, an exclusive OR(XOR) operation is performed on the image and the encryption key to initially diffuse the image. Finally, the image is again diffused using the method of ortho XOR. Simulation analysis shows that the algorithm can achieve good encryption effect, simple and easy implementation, and good security. In the digital image communication transmission, it has good practical value.

Improved Relative-transformation Principal Component Analysis Based on Mahalanobis Distance and Its Application for Fault Detection

主要部件分析(PCA ) 广泛地在过程工业被使用了，它能维持最大的差错察觉率。尽管许多问题在 PCA 被处理了，一些必要问题仍然保持未解决。这研究以下列方法为差错察觉性能改进 PCA。第一，一个相对转变计划基于 Mahalanobis 距离(MD ) 被介绍消除数据的尺寸的效果而不是无尺寸的标准化，并且改进精确性和差错察觉的即时性能。理论推导证明那相对转变能直接基于 MD 消除尺寸的效果并且在结果显示出的相对空间，分析和模拟给 PCA 的合理解释它的优势和有效性。第二，一个改进摆平的预言错误(SPE ) 统计数值被给改进标准化 PCA 的差错察觉表演，它能使标准化基于 PCA 的差错察觉方法成为对实际工业过程合适的更多。最后，二个改进方法被联合更有效地检测差错。建议方法被使用在热连续滚动过程检测 looper 系统的单个差错和多差错，模拟结果以易感知，精确性和差错察觉的即时性能为差错察觉性能表明这些改进的有效性。

THE IMPROVED ISOPARAMETRIC TRANSFORMATION IN BEM

The midside node sensitivity of eight-node isoparametric element in 3-D BEM is investigated. The paper points out that the suggestion, based upon which the midside nodes should be located in the middle third of distance between the adjacent corners, should be followed even more strictly for the conventional isoparametric transformation (CIT) in BEM as that in FEM. A new coordinate transformation relation has been put forward to solve the singular integral problem. The computation is carried to two cases: a cubic body subjected to tensile stress and pure bending. The numerical results show that the improved isoparametric transformation (IIT) is easier and more flexible to practice.

Development of powdery mildew resistant derivatives of wheat variety Fielder for use in genetic transformation

Genetic transformation is widely used to improve target traits and to study gene function in wheat.However,transformation efficiency depends on the physiological status of the recipient genotype and that is affected by several factors including powdery mildew(PM)infection.The widely used recipient variety Fielder is very susceptible to PM.Therefore,it would be beneficial to develop PM resistant derivatives with high regeneration ability for use in genetic transformation.In the present study PM resistant lines CB037 and Pm97033 carrying genes Pm21 and PmV,respectively,were backcrossed to Fielder with selection for PM resistance.Five lines,NT89,NT90,NT154,and WT48 with Pm21 and line FL347 with PmV were developed,identified by molecular markers and genomic in situ hybridization(GISH)or fluorescent in situ hybridization(FISH),and further subjected to detailed assessment of agronomic traits and regeneration ability following genetic transformation capacity.Lines FL347,WT48,NT89 and NT154 assessed as being equal to,or superior,to Fielder in regeneration and transformation ability are recommended as suitable materials for the replacement of Fielder for wheat gene transfer and genome editing study.

An Improved Chirplet Transform and Its Application for Harmonics Detection

The chirplet transform is the generalization form of fast Fourier transform , short-time Fourier transform, and wavelet transform. It has the most flexible time frequency window and successfully used in practices. However, the chirplet transform has not inherent inverse transform, and can not overcome the signal reconstructing problem. In this paper, we proposed the improved chirplet transform (ICT) and constructed the inverse ICT. Finally, by simulating the harmonic voltages, The power of the improved chirplet transform are illustrated for harmonic detection. The contours clearly showed the harmonic occurrence time and harmonic duration.

基于Transformer改进的Faster RCNN在复杂环境下的车辆检测

在监控视角中目标车辆较小、遮挡较为严重,导致检测精度低。通过探讨卷积神经网络和Transformer模型的互相借鉴和联系,并结合损失函数等常规改进,提出了新的Faster RCNN模型。通过借鉴Transformer模型的思想,对原有的特征提取网络进行了改进,将原block比例3∶4∶6∶3改为3∶3∶27∶3、卷积核由3×3改为7×7,增大其感受野,能够更好捕捉图像中的全局特征,使用DW卷积来减少参数量并略微提高性能,使用Channel shuffle解决通道间信息不交流的问题。将原先交并比IoU改为CIoU,与改进后的特征提取网络结合,进一步提高小目标和遮挡目标的检测效果。在UA-DETRAC数据集上,改进后的模型在mAP@0.5:0.95方面比原算法提高了20.20%,并在大、中、小目标下分别提高了15.8%、23%和45.8%,相较于其他模型,如YO⁃LOv7、YOLOv5和Cascade RCNN,mAP@0.5:0.95分别提高了3.3%、5%和6.69%。

Fault Attribute Reduction of Oil Immersed Transformer Based on Improved Imperialist Competitive Algorithm

The original fault data of oil immersed transformer often contains a large number of unnecessary attributes,which greatly increases the elapsed time of the algorithm and reduces the classification accuracy,leading to the rise of the diagnosis error rate.Therefore,in order to obtain high quality oil immersed transformer fault attribute data sets,an improved imperialist competitive algorithm was proposed to optimize the rough set to discretize the original fault data set and the attribute reduction.The feasibility of the proposed algorithm was verified by experiments and compared with other intelligent algorithms.Results show that the algorithm was stable at the 27th iteration with a reduction rate of 56.25%and a reduction accuracy of 98%.By using BP neural network to classify the reduction results,the accuracy was 86.25%,and the overall effect was better than those of the original data and other algorithms.Hence,the proposed method is effective for fault attribute reduction of oil immersed transformer.

从U-Net到Transformer:深度模型在医学图像分割中的应用综述

精准分割医学图像中的病灶对医生探寻病因和制定诊疗方案起关键作用,计算机视觉技术的发展促使深度学习在医学图像分割领域衍生出多种模型架构。U-Net架构以其巧妙的跳跃连接、易于优化的模块设计成为这一领域的基准模型。然而,U-Net以卷积神经网络(CNN)为主干,在长期建模依赖关系方面只擅长获取局部特征,基于CNN的各项方法在执行分割任务中缺乏对图像长期相关性的解释,无法提取全局特征。为帮助本领域学者了解U-Net的发展历程及研究现状,以问题为导向对2016-2023年U-Net改进工作进行综述。首先,从改进结构位置的角度对U-Net及其各项改进模型进行叙述,探讨各工作的研究目的和创新设计及不足之处;其次,对Transformer与U-Net的结合方式进行分析,从中获取改进工作的研究动向;最后,在Synapse和ACDC数据集上进行对比实验,通过实验分析和可视化结果表明,Transformer方法在分割精度方面有显著优势,特别是混合网络子块的结合方式,在确保模型性能的同时兼顾效率,证明了该类工作有着广阔的发展前景和研究价值。

考虑多维影响因素的改进Transformer-PSO短期电价预测方法

随着多元化电力市场的建设,电价影响因素日益增加,市场环境变化也更加剧烈.为提高市场短期电价的预测精度,提出一种考虑多种电价影响因素的改进Transformer-粒子群优化(PSO)算法短期电价预测方法.首先,在考虑历史电价、负荷的基础上进一步分析电价形成的相关因素,利用自相关函数分析电价的多周期特性并在此基础上调整输入序列,克服了仅采用历史数据以及经验调整输入序列导致预测精度受限的问题.其次,结合长短期记忆(LSTM)、自注意力机制与多层注意力机制并采用多输入结构建立改进Transformer模型,进一步提升LSTM模型捕获不同时间步信息间的长短期依赖关系的能力,克服LSTM的信息利用瓶颈,适应包括历史电价及多种电价成因的复杂多序列输入.此外,还利用PSO智能算法搜索模型不同学习阶段的最佳学习率克服手动调整学习率的局限性.最后,采用PJM市场电价进行算例分析,结果表明所提短期电价预测模型能应用于电价影响因素多、变化剧烈的市场环境,并有效提升短期电价预测精度.

Detection of broken manhole cover using improved Hough and image contrast

The damage or loss of urban road manhole covers may cause great risk to residents＇ lives and property if they cannot be discovered in time. Most existing research recommendations for solving this problem are difficult to implement. This paper proposes an algorithm that combines the improved Hough transform and image comparison to identify the damage or loss of the manhole covers in complicated surface conditions by using existing urban road video images. Focusing on the pre-processed images, the edge contour tracking algorithm is applied to find all of the edges. Then with the improved Hough transformation, color recognition and image matching algorithm, the manhole cover area is found and the change rates of the manhole cover area are calculated. Based on the threshold of the change rates, it can be determined whether there is potential damage or loss in the manhole cover. Compared with the traditional Hough transform, the proposed method can effectively improve the processing speed and reduce invalid sampling and accumulation. Experimental results indicate that the proposed algorithm has the functions of effective positioning and early warning in the conditions of complex background, different perspectives, and different videoing time and conditions, such as when the target is partially covered.

基于改进Transformer的生成式文本摘要模型

基于循环神经网络(recurrent neural network,RNN)注意力机制的序列到序列模型在摘要信息提取服务中已经取得了较好的应用,但RNN不能较好地捕捉长时序信息,这使现有模型受限。为此,提出了基于改进Transformer的生成式文本摘要模型。该模型利用Transformer提取全局语义,使用局部卷积提取器提取原文细粒度特征,并设计全局门控单元以防止信息冗余和弥补语义不足,筛选出利于摘要生成的文本关键信息。实验结果表明,所提模型在大规模中文短文本摘要数据集(large scale Chinese short text summarization,LCSTS)上的效果有明显的提升,在电力运维摘要数据集上,也有良好效果,具有可扩展性。

The improved ICA algorithm and its application in the seismic data denoising

The field seismic data is disturbed by the interferential information, which has low signal to noise ratio (SNR). That is disadvantage for seismic data interpretation. So it is important to remove the noise of seismic data. Independent component analysis (ICA) can remove most of the noise interference. However, ICA has some defects in noise reduction, because it needs some conditions that seismic data is independent reciprocally for denoising. To solve these defects, this paper proposes an improved ICA algorithm to noise reduction. Through simulation experiments, it can be obtained that the best decomposition levels of the new algorithm is 3. At last, the proposed improved ICA is applied to deal with the actual seismic data. The results show that it can effectively eliminate most of seismic noise such as random noise, linear interference, surface waves, and so on. The improved ICA is not only easy to denoising, but also has excellent mathematical theoretical properties.

Improved TQWT for marine moving target detection

Under the conditions of strong sea clutter and complex moving targets,it is extremely difficult to detect moving targets in the maritime surface.This paper proposes a new algorithm named improved tunable Q-factor wavelet transform(TQWT)for moving target detection.Firstly,this paper establishes a moving target model and sparsely compensates the Doppler migration of the moving target in the fractional Fourier transform(FRFT)domain.Then,TQWT is adopted to decompose the signal based on the discrimination between the sea clutter and the target’s oscillation characteristics,using the basis pursuit denoising(BPDN)algorithm to get the wavelet coefficients.Furthermore,an energy selection method based on the optimal distribution of sub-bands energy is proposed to sparse the coefficients and reconstruct the target.Finally,experiments on the Council for Scientific and Industrial Research(CSIR)dataset indicate the performance of the proposed method and provide the basis for subsequent target detection.

Improved HHT and its application in narrowband radar imaging for precession cone-shaped targets

Imaging the spatial precession cone-shaped targets with narrowband radar is a new technical approach in mid-course recognition problem. However, most existing time-frequency methods still have some inevitable deficiencies for extracting microDoppler information in practical applications, which leads to blurring of the image. A new narrowband radar imaging algorithm for the precession cone-shaped targets is proposed. The instantaneous frequency of each scattering point is gained by using the improved Hilbert-Huang transform, then the positions of scattering points in the parameter domain are reconstructed. Numerical simulation and experiment results confirm the effectiveness and high precision of the proposed algorithm.

An improved bearing fault detection strategy based on artificial bee colony algorithm

The operating state of bearing affects the performance of rotating machinery;thus,how to accurately extract features from the original vibration signals and recognise the faulty parts as early as possible is very critical.In this study,the one‐dimensional ternary model which has been proved to be an effective statistical method in feature selection is introduced and shapelet transformation is proposed to calculate the parameter of one‐dimensional ternary model that is usually selected by trial and error.Then XGBoost is used to recognise the faults from the obtained features,and artificial bee colony algorithm(ABC)is introduced to optimise the parameters of XGBoost.Moreover,for improving the performance of intelligent algorithm,an improved strategy where the evolution is guided by the probability that the optimal solution appears in certain solution space is proposed.The experimental results based on the failure vibration signal samples show that the average accuracy of fault signal recognition can reach 97%,which is much higher than the ones corresponding to traditional extraction strategies.And with the help of improved ABC algorithm,the performance of XGBoost classifier could be optimised;the accuracy could be improved from 97.02%to 98.60%compared with the traditional classification strategy.

Intelligent Fusion of Infrared and Visible Image Data Based on Convolutional Sparse Representation and Improved Pulse-Coupled Neural Network

Multi-source information can be obtained through the fusion of infrared images and visible light images,which have the characteristics of complementary information.However,the existing acquisition methods of fusion images have disadvantages such as blurred edges,low contrast,and loss of details.Based on convolution sparse representation and improved pulse-coupled neural network this paper proposes an image fusion algorithm that decompose the source images into high-frequency and low-frequency subbands by non-subsampled Shearlet Transform(NSST).Furthermore,the low-frequency subbands were fused by convolutional sparse representation(CSR),and the high-frequency subbands were fused by an improved pulse coupled neural network(IPCNN)algorithm,which can effectively solve the problem of difficulty in setting parameters of the traditional PCNN algorithm,improving the performance of sparse representation with details injection.The result reveals that the proposed method in this paper has more advantages than the existing mainstream fusion algorithms in terms of visual effects and objective indicators.

Trial Equation Method for Solving the Improved Boussinesq Equation

Trial equation method is a powerful tool for obtaining exact solutions of nonlinear differential equations. In this paper, the improved Boussinesq is reduced to an ordinary differential equation under the travelling wave transformation. Trial equation method and the theory of complete discrimination system for polynomial are used to establish exact solutions of the improved Boussinesq equation.

Research and Implementation for License Plate Recognition Based on Improved Projection Algorithm

This paper analyzes and dissertates the discrete wavelet transform and improved projection algorithm in four kernel stages （image preprocessing, license plate localization, character segmentation, license plate recognition, i.e.） of license plate recognition system in detail. At last, it gives some conclusions and suggestions on future research.