Point cloud segmentation (PCS) aims to make per-point predictions, enabling robots and autonomous driving cars to understand their environments. The range image is a dense representation of a large-scale outdoor point cloud. Segmentation models built upon the range image commonly execute efficiently. However, the projection of the point cloud onto the range image inevitably leads to dropping points because multiple points could be projected onto the same image location, but only one point is kept. More importantly, it is challenging to assign correct predictions to the dropped points with different classes from the kept point class. Besides, existing post-processing methods, such as K-nearest neighbor (KNN) search and kernel point convolution (KPConv), cannot be trained with the models in an end-to-end manner or cannot process varying-density outdoor point clouds well, thereby enabling the models to achieve sub-optimal performance. To alleviate this problem, we propose a trainable pointwise decoder module (PDM) as the post-processing approach, which gathers weighted features from the neighbors and then makes the final prediction for the query point. In addition, we introduce a virtual range image-guided copy-rotate-paste (VRCrop) strategy in data augmentation. VRCrop constrains the total number of points and eliminates undesirable artifacts in the augmented point cloud. Also, VRCrop is model-agnostic and can be easily employed in various PCS models. With PDM and VRCrop, existing range image-based segmentation models consistently surpass their counterparts on the SemanticKITTI, SemanticPOSS, and nuScenes datasets.

Point cloud segmentation (PCS) is to classify each point in point clouds. The task enables robots to parse their 3D surroundings and run autonomously. According to different point cloud representations, existing PCS models can be roughly divided into point-, voxel-, and range image-based models. However, no work has been found to report comprehensive comparisons among the state-of-the-art point-, voxel-, and range image-based models from an application perspective, bringing difficulty in utilizing these models for real-world scenarios. In this paper, we provide thorough comparisons among the models by considering the LiDAR data motion compensation and the metrics of model parameters, max GPU memory allocated during testing, inference latency, frames per second, intersection-over-union (IoU) and mean IoU (mIoU) scores. The experimental results benefit engineers when choosing a reasonable PCS model for an application and inspire researchers in the PCS field to design more practical models for a real-world scenario.

Point cloud segmentation (PCS) aims to classify each point in a point cloud. The task plays an important role in robotics and remote sensing. However, existing copy-paste and copy-rotate-paste augmentation techniques cause undesirable artifacts in the augmented point cloud and cannot effectively copy and paste interesting objects from the whole training dataset, bringing difficulty in training image-points fused models and leading to sub-optimal PCS performance. In this paper, we propose an improved virtual range image-guided copy-rotate-paste (VRCrop++) strategy and a global copy-rotate-paste (GCrop) technique. VRCrop++ and GCrop remove unwanted artifacts in the augmented point cloud by a simple but effective “point-to-patch” strategy in the pasting step. Besides, GCrop copies the objects globally with the reciprocal of the point distribution ratio and effectively pastes the objects considering the minimal overlapping region. Extensive experiments conducted on SemanticKITTI and SemanticPOSS datasets demonstrate that with VRCrop++ and GCrop, the existing range image-points fused models consistently surpass their counterparts.

Point cloud segmentation (PCS) aims to separate points into different and meaningful groups. The task plays an important role in robotics because PCS enables robots to understand their physical environments directly. To process sparse and large-scale outdoor point clouds in real time, range image-based models are commonly adopted. However, in a range image, the lack of explicit depth information inevitably causes some separate objects in 3D space to touch each other, bringing difficulty for the range image-based models in correctly segmenting the objects. Moreover, previous PCS models are usually derived from the existing color image-based models and unable to make full use of the implicit but ordered depth information inherent in the range image, thereby achieving inferior performance. In this paper, we propose Depth-Aware Module (DAM) and Fast FMVNet V3. DAM perceives the ordered depth information in the range image by explicitly modelling the interdependence among channels. Fast FMVNet V3 incorporates DAM by integrating it into the last block in each architecture stage. Extensive experiments conducted on SemanticKITTI, nuScenes, and SemanticPOSS demonstrate that DAM brings a significant improvement for Fast FMVNet V3 with negligible computational cost.

Point cloud segmentation (PCS) plays an essential role in robot perception and navigation tasks. To efficiently understand large-scale outdoor point clouds, their range image representation is commonly adopted. This image-like representation is compact and structured, making range image-based PCS models practical. However, undesirable missing values in the range images damage the shapes and patterns of objects. This problem creates difficulty for the models in learning coherent and complete geometric information from the objects. Consequently, the PCS models only achieve inferior performance. Delving deeply into this issue, we find that the use of unreasonable projection approaches and deskewing scans mainly leads to unwanted missing values in the range images. Besides, almost all previous works fail to consider filling in the unexpected missing values in the PCS task. To alleviate this problem, we first propose a new projection method, namely scan unfolding++ (SU++), to avoid massive missing values in the generated range images. Then, we introduce a simple yet effective approach, namely range-dependent K-nearest neighbor interpolation (KNNI), to further fill in missing values. Finally, we introduce the Filling Missing Values Network (FMVNet) and Fast FMVNet. Extensive experimental results on SemanticKITTI, SemanticPOSS, and nuScenes datasets demonstrate that by employing the proposed SU++ and KNNI, existing range image-based PCS models consistently achieve better performance than the baseline models. Besides, both FMVNet and Fast FMVNet achieve state-of-the-art performance in terms of the speed-accuracy trade-off. The proposed methods can be applied to other range image-based tasks and practical applications.

Semantic segmentation (SS) aims to classify each pixel into one of the pre-defined classes. This task plays an important role in self-driving cars and autonomous drones. In SS, many works have shown that most misclassified pixels are commonly near object boundaries with high uncertainties. However, existing SS loss functions are not tailored to handle these uncertain pixels during training, as these pixels are usually treated equally as confidently classified pixels and cannot be embedded with arbitrary low distortion in Euclidean space, thereby degenerating the performance of SS. To overcome this problem, this paper designs a Hyperbolic Uncertainty Loss (HyperUL), which dynamically highlights the misclassified and high-uncertainty pixels in Hyperbolic space during training via the hyperbolic distances. The proposed HyperUL is model agnostic and can be easily applied to various neural architectures. After employing HyperUL to three recent SS models, the experimental results on Cityscapes, UAVid, and ACDC datasets reveal that the segmentation performance of existing SS models can be consistently improved. Additionally, reliable measurement of model uncertainty plays a key role in real-world applications such as autonomous controls of vehicles and drones. To meet this requirement, we propose the Hyperbolic Uncertainty Estimation method, which is easily implemented by only post-processing the generated Hyperbolic embeddings. By this approach, we can calculate the uncertainty values almost for free. Quantitative and qualitative results on Cityscapes, UAVid, and ACDC datasets verify that our proposed uncertainty estimation method usually outputs more meaningful results compared with popular MC-dropout and ensembling methods.

Point cloud segmentation (PCS) aims to make per-point predictions and enables robots and autonomous driving cars to understand the environment. The range image is a dense representation of a large-scale outdoor point cloud, and segmentation models built upon the image commonly execute efficiently. However, the projection of the point cloud onto the range image inevitably leads to dropping points because, at each image coordinate, only one point is kept despite multiple points being projected onto the same location. More importantly, it is challenging to assign correct predictions to the dropped points that belong to the classes different from the kept point class. Besides, existing post-processing methods, such as K-nearest neighbor (KNN) search and kernel point convolution (KPConv), cannot be trained with the models in an end-to-end manner or cannot process varying-density outdoor point clouds well, thereby enabling the models to achieve sub-optimal performance. To alleviate this problem, we propose a trainable pointwise decoder module (PDM) as the post-processing approach, which gathers weighted features from the neighbors and then makes the final prediction for the query point. In addition, we introduce a virtual range image-guided copy-rotate-paste (VRCrop) strategy in data augmentation. VRCrop constrains the total number of points and eliminates undesirable artifacts in the augmented point cloud. With PDM and VRCrop, existing range image-based segmentation models consistently perform better than their counterparts on the SemanticKITTI, SemanticPOSS, and nuScenes datasets.

Semantic segmentation (SS) partitions an image into several coherent semantically meaningful parts and classifies each part into one of the pre-determined classes. In this paper, we argue that the existing SS methods cannot be reliably applied to autonomous driving system as they ignore the different importance levels of distinct classes for safe driving. For example, pedestrian, car, and bicyclist in the scene are much more important than sky and building when driving a car, so their segmentations should be as accurate as possible. To incorporate the importance information possessed by various object classes, this paper designs an “importance-aware loss” (IAL) that specifically emphasizes the critical objects for autonomous driving. The IAL operates under a hierarchical structure and the classes with different importance are located in different levels so that they are assigned distinct weights. Furthermore, we derive the forward and backward propagation rules for IAL and apply them to four typical deep neural networks for realizing SS in an intelligent driving system. The experiments on CamVid and Cityscapes data sets reveal that, by employing the proposed loss function, the existing deep learning models, including FCN, SegNet, ENet, and ERFNet, are able to consistently obtain the improved segmentation results on the pre-defined important classes for safe driving.

针对非洲野狗算法求解优化问题时全局性收敛不强的特点,对该算法进行改进,提出了改进的非洲野狗算法,结合二进制编码设计了求解离散优化问题的二进制编码非洲野狗算法,并将该算法应用于求解TSP问题并与其他算法做对比分析.研究结果显示,求解TSP问题时二进制编码非洲野狗算法求解精度更高,收敛速度更快.

Semantic Segmentation (SS) partitions an image into several coherent semantically meaningful parts, and classifies each part into one of the pre-determined classes. In this paper, we argue that existing SS methods cannot be reliably applied to autonomous driving system as they ignore the different importance levels of distinct classes for safe-driving. For example, pedestrians in the scene are much more important than sky when driving a car, so their segmentations should be as accurate as possible. To incorporate the importance information possessed by various object classes, this paper designs an "Importance-Aware Loss" (IAL) that specifically emphasizes the critical objects for autonomous driving. IAL operates under a hierarchical structure, and the classes with different importance are located in different levels so that they are assigned distinct weights. Furthermore, we derive the forward and backward propagation rules for IAL and apply them to deep neural networks for realizing SS in intelligent driving system. The experiments on CamVid and Cityscapes datasets reveal that by employing the proposed loss function, the existing deep learning models including FCN, SegNet and ENet are able to consistently obtain the improved segmentation results on the pre-defined important classes for safe-driving.

目前的评标系统大多基于单项评议法、综合评议法、两阶段评议法等传统方法，评标方法简单但主观因素影响较大。本文提出了基于层次分析与模糊聚类的建筑工程评标的方法，研究了方法的理论基础，给出了评标方法及流程，运用该方法使得评标更具客观性。

针对一般边坡失稳预测方法的样本归一化处理、选择经验核函数等带来的预测误差，本文利用果蝇优化算法优化支持向量回归的回归参数（ FOA-SVR ），选择“bspline”核函数，对于边坡训练样本不做归一化处理，通过训练来对边坡稳定性进行预测，并且利用MATLAB编程将上述算法可视化，开发出了基于FOA-SVR的边坡预测软件。算例的计算结果显示，预测值和实际值一致，算法精度较高，而且软件系统稳定，界面简洁，具有很强的实用性。

Water quality evaluation is a hot issue in the present study,which is part of a comprehensive evaluation of multiple factors.In this paper,the comprehensive evaluation of water quality of a certain river in southern China is conducted by using artificial bee colony-projection pursuit.The sampling time is from March to April,2013 and the sampling sites is in the upstream,midstream and downstream of the river.Six evaluation indicators,i.e.ammonia nitrogen,total phosphorus,total nitrogen,COD,nitrate and nitrite,are selected according to the measured data.A comprehensive evaluation model is establised by using artificial bee colony-projection pursuit and referencing to the national surface water environmental standards.The above model is solved by MATLAB.The evaluation result is that from upstream to downstream,the water quality is at the 1st,4th and 5th grade of the national surface water quality standard.It will provide a scientific basis to develop water pollution control decisions.

电子技术的发展，带来环境的巨大的污染，对人们的生活也产生了很大的影响，对土壤，大气，水都造成了严重的破坏，对生态带来了巨大的破坏，影响人们的健康生活，必须要采取治理措施，才能够很好的治理污染。

为减小由于二进制编码的舍入误差对该问题计算结果的影响,对求解回归支持向量机的一种调节熵方法进行了区间扩张,讨论了区间函数的相关定理与收敛性.对设计的区间算法做了收敛性证明,并给出了数值实验,验证了方法与算法的可行性和有效性.

In this paper ,a Projection Pursuit Method ,Wolf Colony Search Algorithm based on leader strategy is used for comprehen‐sive evaluation of a northern river water quality .The sampling time is February to April of 2013 ;the five sampling points were se‐lected in the upstream ,mid-upstream ,midstream ,mid-downstream and downstream of the river ;six water quality indicators ,inclu‐ding ammonia nitrogen ,total phosphorus ,total nitrogen ,COD ,nitrate and nitrite ,are selected .Projection Pursuit Method ,Wolf Colony Search Algorithm based on leader strategy ,is used to establish a comprehensive evaluation model ,which is solved by combi‐ning with MATLAB programming .Referencing to GB Surface Water Environmental Standards ,the evaluation result is obtained that the water quality level from upstream to downstream is the grade 2 ,2 ,4 ,5 ,5 ,respectively .This provides a scientific basis for water pollution control decision-making .

This paper discussed unconstrained nonlinear lpproblem from signal processing,system identification and other areas. In order to reduce the affection of the binary coded rounding errors on the calculation results of the problem,it introduced interval extension into the maximum entropy method. The new maximum entropy function improved by interval extension had at least second order convergence. It designed an interval algorithm with polynomial time complexity to solve some examples for the numerical solutions. The numerical results show that this new interval algorithm is reliable and the numerical solution using interval extension are more accurate.

文章构造了求解非线性lp问题的新方法——量子遗传法.给出了具体的量子遗传算法步骤,并且结合文献[6]相关算例,将用量子遗传算法求解出的结果与其对比分析,结果显示量子遗传算法求在解非线性lp问题是十分有效和实用的.