忆•热巴舞蹈教学体系建构历程

热巴舞蹈是西藏传统民间舞蹈之一,它民族风格浓郁,特色鲜明,是西藏文化艺术的重要组成部分。本文通过回忆作者学习热巴舞蹈以及笔者进行热巴舞蹈教学体系建构的历程,以期对当今热巴舞蹈学习者起到一定的鼓励作用。

基于核优化密度聚类的绿色苹果分割算法

苹果的可见光谱目标的高效、精准识别是实现果园测产或机器自动采摘作业的关键,由于绿色目标果实与枝叶背景颜色较为相近,因此绿色苹果的识别成为新的挑战。再由于果园实际复杂环境因素影响,如光照、阴雨、枝叶遮挡、目标重叠等情况,现有的目标果实识别方案难以满足测产或自动采摘的实时、精准作业需求。为更好地实现果园自然环境中绿色目标果实识别问题,提出一种新的核密度估计优化的聚类分割算法(kernel density clustering,KDC)。新算法首先利用简单的迭代聚类(simple linear iterative cluster,SLIC)算法将目标图像分割成不规则块,集结小区域内近似像素点组成超像素区域,计算单元由像素点转变为超像素区域,有效降低数据复杂度,且SLIC算法简化图像数据时可有效避免目标果实轮廓模糊;基于超像素构造R-B区域均值和G-B区域均值的二维特征分量,建立针对聚类分析的青苹果颜色特征空间。然后借助密度峰值聚类中心计算绿色苹果图像每个数据点的局部密度和局部差异度,为解决分割边界模糊问题,在计算过程中利用核密度估计计算局部密度,确保局部密度在不同复杂场景中的清晰准确表达,以更精准找出被低密度区域分割的高密度区域,实现任意形状的聚类。最后以局部密度和距离构造寻找聚类中心的决策图,该研究采用双排序算法实现聚类中心的自动选择,完成目标果实的高效分割。新算法通过SLIC算法获得图像的超像素区域表示,数据点的局部密度通过核密度估计得到,大幅降低算法的计算量,实现目标图像的高效、精准分割。为更好地验证新算法性能,实验采集多光照、阴雨等环境下的遮挡、重叠等复杂目标图像,以分割效率、分割有效性、假阳性、假阴性等指标进行评价,通过对比k-means聚类算法、meanshift聚类算法、FCM算法和DPCA算法,该研究提出的新算法分割性能均最优。

“首届全区热巴舞展演暨热巴艺术高峰论坛”综述及其他

热巴舞蹈作为藏族康区当今最具活跃的舞蹈种类之一,诉说着本民族数千年的历史文化变革,彰显着我们先辈们的聪明才智,热巴舞蹈从"远古"的苯教孕育而生,在辉煌的今天茁壮成长,展现了雪域民族的自强不息的生命力。首届全区热巴舞展演暨热巴艺术高峰论坛是践行建设社会主义文化强国,推动社会主义文化大发展、大繁荣精神,传播非遗艺术文化,树立民族自信的重要活动。而高等艺术教育运用高校这一稳定平台为这一非遗文化的传承和发展再一次架上翱翔的翅膀。通过美育教育、非遗文化进校园,让我们更进一步的感受到了民族文化沉淀千年的艺术魅力。

Model Implementation and Analysis of a True Three-dimensional Display System

To model a true three-dimensional(3D)display system,we introduced the method of voxel molding to obtain the stereoscopic imaging space of the system.For the distribution of each voxel,we proposed a four-dimensional(4D)Givone–Roessor(GR)model for state-space representation—that is,we established a local state-space model with the 3D position and one-dimensional time coordi-nates to describe the system.First,we extended the original elementary operation approach to a 4D condition and proposed the implementation steps of the realiza-tion matrix of the 4D GR model.Then,we described the working process of a true 3D display system,analyzed its real-time performance,introduced the fixed-point quantization model to simplify the system matrix,and derived the conditions for the global asymptotic stability of the system after quantization.Finally,we provided an example to prove the true 3D display system’s feasibility by simulation.The GR-model-representation method and its implementation steps proposed in this paper simplified the system’s mathematical expression and facilitated the microcon-troller software implementation.Real-time and stability analyses can be used widely to analyze and design true 3D display systems.

Benchmarking visual SLAM methods in mirror environments

Visual simultaneous localisation and mapping(vSLAM)finds applications for indoor and outdoor navigation that routinely subjects it to visual complexities,particularly mirror reflections.The effect of mirror presence(time visible and its average size in the frame)was hypothesised to impact localisation and mapping performance,with systems using direct techniques expected to perform worse.Thus,a dataset,MirrEnv,of image sequences recorded in mirror environments,was collected,and used to evaluate the performance of existing representative methods.RGBD ORB-SLAM3 and BundleFusion appear to show moderate degradation of absolute trajectory error with increasing mirror duration,whilst the remaining results did not show significantly degraded localisation performance.The mesh maps generated proved to be very inaccurate,with real and virtual reflections colliding in the reconstructions.A discussion is given of the likely sources of error and robustness in mirror environments,outlining future directions for validating and improving vSLAM performance in the presence of planar mirrors.The MirrEnv dataset is available at https://doi.org/10.17035/d.2023.0292477898.

Learning to bag with a simulation‐free reinforcement learning framework for robots

Bagging is an essential skill that humans perform in their daily activities.However,deformable objects,such as bags,are complex for robots to manipulate.A learning-based framework that enables robots to learn bagging is presented.The novelty of this framework is its ability to learn and perform bagging without relying on simulations.The learning process is accomplished through a reinforcement learning(RL)algorithm introduced and designed to find the best grasping points of the bag based on a set of compact state representations.The framework utilises a set of primitive actions and represents the task in five states.In our experiments,the framework reached 60% and 80% success rates after around 3 h of training in the real world when starting the bagging task from folded and unfolded states,respectively.Finally,the authors test the trained RL model with eight more bags of different sizes to evaluate its generalisability.

甘孜高原地区重度子痫前期累计发病率调查及影响因素分析

目的:调查甘孜高原地区重度子痫前期累计发病率并分析其影响因素。方法:对2020年1月至2021年6月在甘孜高原地区进行体检的孕妇进行资料采集及问卷调查,分析其累计发病率和影响因素。结果:3 443例孕妇中有150例孕妇确诊为重度子痫前期,累计发病率为4.36%。将150例重度子痫前期孕妇作为病例组,同期选取116例体检健康孕妇作为对照组进行分析,结果显示,组间孕妇年龄、文化程度、居住海拔≥3 000 m、产检不规范、藏族等因素比较,差异具有统计学意义(P<0.05),但是孕周、月经状况、职业以及产前是否接受准妈妈教育等因素比较,差异无统计学意义(P>0.05);年龄>35岁、高中及以下学历、居住海拔≥3 000 m居住地、产检不规范、藏族为影响重度子痫前期患病的因素(P<0.05)。结论:甘孜高原地区重度子痫前期累计发病率为4.36%,年龄>35岁、高中及以下学历、居住海拔≥3 000 m、产检不规范、藏族为影响患病的因素,建议加强孕期监测,预防重度子痫前期的发生,以改善孕妇健康状况。

BFP Net: Balanced Feature Pyramid Network for Small Apple Detection in Complex Orchard Environment

Despite of significant achievements made in the detection of target fruits,small fruit detection remains a great challenge,especially for immature small green fruits with a few pixels.The closeness of color between the fruit skin and the background greatly increases the difficulty of locating small target fruits in the natural orchard environment.In this paper,we propose a balanced feature pyramid network(BFP Net)for small apple detection.

FCOS-LSC:A Novel Model for Green Fruit Detection in a Complex Orchard Environment

To better address the difficulties in designing green fruit recognition techniques in machine vision systems,a new fruit detection model is proposed.This model is an optimization of the FCOS(full convolution one-stage object detection)algorithm,incorporating LSC(level scales,spaces,channels)attention blocks in the network structure,and named FCOS-LSC.The method achieves efficient recognition and localization of green fruit images affected by overlapping occlusions,lighting conditions,and capture angles.Specifically,the improved feature extraction network ResNet50 with added deformable convolution is used to fully extract green fruit feature information.The feature pyramid network(FPN)is employed to fully fuse low-level detail information and high-level semantic information in a cross-connected and top-down connected way.Next,the attention mechanisms are added to each of the 3 dimensions of scale,space(including the height and width of the feature map),and channel of the generated multiscale feature map to improve the feature perception capability of the network.Finally,the classification and regression subnetworks of the model are applied to predict the fruit category and bounding box.In the classification branch,a new positive and negative sample selection strategy is applied to better distinguish supervised signals by designing weights in the loss function to achieve more accurate fruit detection.The proposed FCOS-LSC model has 38.65M parameters,38.72G floating point operations,and mean average precision of 63.0%and 75.2%for detecting green apples and green persimmons,respectively.In summary,FCOS-LSC outperforms the state-of-the-art models in terms of precision and complexity to meet the accurate and efficient requirements of green fruit recognition using intelligent agricultural equipment.Correspondingly,FCOS-LSC can be used to improve the robustness and generalization of the green fruit detection models.