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Weakly supervised point cloud segmentation via deep morphological semantic information embedding

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CAAI Transactions on Intelligence Technology 2024年第3期9卷 695-708页

作者： Wenhao Xue Yang Yang Lei Li Zhongling Huang Xinggang Wang junwei han Dingwen Zhang School of Automation Northwestern Polytechnical UniversityXi'anChina Institute of Mechanical and Electrical Engineering BeijingChina School of Electronic Information and Communications Huazhong University of Science and TechnologyWuhanChina

Segmenting the semantic regions of point clouds is a crucial step for intelligent agents to understand 3D *** supervised point cloud segmentation is highly desirable because entirely labelling point clouds is highly time-consuming and *** the low-costing labelling of 3D point clouds,the scene-level label is one of the most effortless label ***,due to the limitation of classifier discriminative capability and the orderless and structurless nature of the point cloud data,existing scene-level method is hard to transfer the semantic information,which usually leads to the under-activated or over-activated *** this end,a local semantic embedding network is introduced to learn local structural patterns and semantic ***,the proposed network contains graph convolution-based dilation and erosion embedding modules to implement‘inside-out’and‘outside-in’semantic information dissemination ***,the proposed weakly supervised learning framework could achieve the mutual propagation of semantic information in the foreground and *** experiments on the widely used ScanNet benchmark demonstrate the superior capacity of the proposed approach when compared to the current alternatives and baseline models.

关键词： artificial intelligence computer vision deep learning

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橙皮还原浸出废旧锂电池正极材料有价金属

中国有色金属学报 2024年第2期34卷 561-572页

作者：谷昆泓覃文庆韩俊伟中南大学资源加工与生物工程学院长沙410083

为了促进废旧锂电池的高效环保浸出,研究了一种利用橙皮作为有机还原剂,高效浸出废旧锂电池正极材料中金属的方法。结果表明:橙皮的添加显著提高了柠檬酸浸出剂的性能,实现了锂、镍、钴和锰的高浸出率,最佳浸出条件为柠檬酸浓度2 mol/L... 详细信息

为了促进废旧锂电池的高效环保浸出,研究了一种利用橙皮作为有机还原剂,高效浸出废旧锂电池正极材料中金属的方法。结果表明:橙皮的添加显著提高了柠檬酸浸出剂的性能,实现了锂、镍、钴和锰的高浸出率,最佳浸出条件为柠檬酸浓度2 mol/L、橙皮用量40 g/L、浸出温度95℃、液固比20∶1、搅拌速度300 r/min、浸出时间120 min。利用Design Expert 10推导出了一个浸出模型,能精确预测金属的浸出率。动力学研究表明,锂、镍、钴和锰的表观活化能分别为64.95、67.71、66.65和72.8 kJ/mol,说明正极材料浸出过程主要受化学反应控制。通过对比OP浸出前后的SEM像,验证了橙皮还原能力可用还原糖理论进行解释。

关键词：废旧锂电池正极材料酸浸活化能橙皮废弃物

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低浓度稀土离子吸附材料研究进展

工程科学学报 2024年第8期46卷 1381-1392页

作者：魏徐一韩俊伟覃文庆中南大学资源加工与生物工程学院长沙410083

近年来,随着稀土产量与需求之间的供需矛盾日益加深,对稀土资源的高效绿色提取和分离提出了更高的要求.特别是从稀土矿选矿废水、精炼废水、海水和温泉等低浓度的稀土溶液中回收稀土资源已成为研究热点.相比于蒸发结晶、化学沉淀、溶剂... 详细信息

近年来,随着稀土产量与需求之间的供需矛盾日益加深,对稀土资源的高效绿色提取和分离提出了更高的要求.特别是从稀土矿选矿废水、精炼废水、海水和温泉等低浓度的稀土溶液中回收稀土资源已成为研究热点.相比于蒸发结晶、化学沉淀、溶剂萃取等传统提取技术,吸附法因具有操作简单、成本低廉、处理量大和适应性强等优势,成为低浓度稀土深度回收的潜在方法之一.本文汇总了近年来国内外有关于稀土离子吸附材料的研究进展,详细介绍了矿物基、碳基、金属−有机框架基和高分子基等吸附材料的设计与合成思路、微观形貌、吸附行为、材料性能和潜在的应用潜力.最后,对比指出了各种吸附材料的优势和不足,提出开发面向应用的高效绿色靶向吸附材料是未来稀土吸附材料的主要发展趋势,靶向吸附技术也将成为低浓度稀土溶液(废水)资源化的主要方法,以期为稀土资源的高效利用提供参考.

关键词：稀土元素吸附材料水处理资源循环研究进展

基于时频分析的SAR目标微波视觉特性智能感知方法与应用

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雷达学报（中英文） 2024年第2期13卷 331-344页

作者：黄钟泠吴冲姚西文王立鹏韩军伟西北工业大学自动化学院西安710072 北京机电工程研究所北京100074

合成孔径雷达(SAR)目标识别智能算法目前仍面临缺少鲁棒性、泛化性和可解释性的挑战,理解SAR目标微波特性并将其结合先进的深度学习算法,实现高效鲁棒的SAR目标识别,是目前领域较为关注的研究重点。SAR目标特性反演方法通常计算复杂度较... 详细信息

合成孔径雷达(SAR)目标识别智能算法目前仍面临缺少鲁棒性、泛化性和可解释性的挑战,理解SAR目标微波特性并将其结合先进的深度学习算法,实现高效鲁棒的SAR目标识别,是目前领域较为关注的研究重点。SAR目标特性反演方法通常计算复杂度较高,难以结合深度神经网络实现端到端的实时预测。为促进SAR目标物理特性在智能识别任务中的应用,发展高效、智能、可解释的微波物理特性感知方法至关重要。该文将高分辨SAR目标的非平稳特性作为一种典型的微波视觉特性,提出一种改进的基于时频分析的目标特性智能感知方法,优化了处理流程和计算效率,使之更适用于SAR目标识别场景,并进一步将其应用到SAR目标智能识别算法中,实现了稳定的性能提升。该方法泛化性强、计算效率高,能得到物理可解释的SAR目标特性分类结果,对目标识别算法的性能提升与属性散射中心模型相当。

关键词：合成孔径雷达(SAR) 目标识别目标特性微波视觉时频分析(TFA)

Task-wise attention guided part complementary learning for few-shot image classification

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Science China(Information Sciences) 2021年第2期64卷 44-57页

作者： Gong CHENG Ruimin LI Chunbo LANG junwei han Research and Development Institute of Northwestern Polytechnical University in Shenzhen School of Automation Northwestern Polytechnical University CETC Key Laboratory of Aerospace Information Applications

A general framework to tackle the problem of few-shot learning is meta-learning, which aims to train a well-generalized meta-learner(or backbone network) to learn a base-learner for each future task with small training data. Although a lot of work has produced relatively good results, there are still some challenges for few-shot image classification. First, meta-learning is a learning problem over a collection of tasks and the meta-learner is usually shared among all tasks. To achieve image classification of novel classes in different tasks, it is needed to learn a base-learner for each task. Under the circumstances, how to make the base-learner specialized, and thus respond to different inputs in an extremely task-wise manner for different tasks is a big challenge at present. Second, classification network usually inclines to identify local regions from the most discriminative object parts rather than the whole objects for recognition, thereby resulting in incomplete feature representations. To address the first challenge, we propose a task-wise attention(TWA)module to guide the base-learner to extract task-specific image features. To address the second challenge,under the guidance of TWA, we propose a part complementary learning(PCL) module to extract and fuse the features of multiple complementary parts of target objects, and thus we can obtain more specific and complete information. In addition, the proposed TWA module and PCL module can be embedded into a unified network for end-to-end training. Extensive experiments on two commonly-used benchmark datasets and comparison with state-of-the-art methods demonstrate the effectiveness of our proposed method.

关键词： few-shot learning meta-learning task-wise attention part complementary learning

Analysis and Application of the Singularity Locus of the Stewart Platform

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Chinese Journal of Mechanical Engineering 2011年第1期24卷 133-140页

作者： MA Jianming HUANG Qitao XIONG Haiguo han junwei State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin 150001 China

The direct use of the determinant of Jacobian matrix being equal to zero for the singularity analysis is generally difficult which is due to complexity of the Jacobian matrix of 6-DOF parallel manipulators,especially for Stewart ***,several scholars make their great contribution to the effective solution of this problem,but neither of them find the right *** paper gives a brief analysis of the kinematics of the Stewart platform and derives the Jacobian matrices of the system through the velocity *** the basis of the traditional classification of singularities,the second type of singularity is *** assumption of any three of the six variables of the Stewart platform as constant is made,then the analytical expression of singularity locus equation of the second type singularity which contains another three pose variables is *** singularity locus is represented in the three-dimensional space through the derived *** correctness and validity of the proposed method are verified through ***,the singularity analysis of an electric Stewart platform in its desired workspace and reachable workspace is ***,one can easily identify whether singularity exists in a given workspace of a Stewart platform and determine whether the existed singularity can be avoided through the proposed *** proposed method also provides theoretical principle for the design and application of the Stewart platform and has great significance for the trajectory planning and control.

关键词： parallel manipulator Stewart platform Jacobian matrix singularity locus workspace

Class attention network for image recognition

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Science China(Information Sciences) 2023年第3期66卷 160-172页

作者： Gong CHENG Pujian LAI Decheng GAO junwei han School of Automation Northwestern Polytechnical University

Visual attention has become a popular and widely used component for image recognition. Although various attention-based methods have been proposed and achieved relatively competitive results, it is observed that the semantic features of each class are likely to entangle with each other, and few studies focus on explicitly extracting category-aware features so far. To address this issue, this paper presents an attention-based image recognition method by using class-specific dictionary learning to disentangle the neural network's outputs into class-dependent features, thus boosting their discrimination abilities. Specifically,we develop a class attention network(CANet) via integrating a simple yet effective class-specific attention encoding(CAE) module on the top of convolutional layers. Given the feature maps of the convolutional neural networks(CNNs), the CAE module learns a class-specific dictionary, which is leveraged to encode attention maps for each category. Then these attention maps are multiplied by the input features for classwise adaptive feature refinement. Extensive experiments on the PASCAL VOC 2007, PASCAL VOC 2012,MS COCO, and CUB-200-2011 datasets demonstrate the fabulous performance of our method on multiple visual recognition tasks, including multi-label image classification and fine-grained visual classification. In addition, the visualization results testify that CNNs can explicitly learn class-wise feature representations by introducing class-specific dictionary learning.

关键词： visual attention class-specific attention encoding class attention network dictionary learning

Densely nested top-down flows for salient object detection

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Science China(Information Sciences) 2022年第8期65卷 57-70页

作者： Chaowei FANG Haibin TIAN Dingwen ZhanG Qiang ZhanG Jungong han junwei han School of Artificial Intelligence Xidian University School of Mechano-Electronic Engineering Xidian University Brain and Artificial Intelligence Laboratory School of AutomationNorthwestern Polytechnical University Department of Computer Science Aberystwyth University

With the goal of identifying pixel-wise salient object regions from each input image, salient object detection(SOD) has been receiving great attention in recent years. One kind of mainstream SOD method is formed by a bottom-up feature encoding procedure and a top-down information decoding procedure. While numerous approaches have explored the bottom-up feature extraction for this task, the design of top-down flows remains under-studied. To this end, this paper revisits the role of top-down modeling in salient object detection and designs a novel densely nested top-down flows(DNTDF)-based framework. In every stage of DNTDF, features from higher levels are read in via the progressive compression shortcut paths(PCSPs). The notable characteristics of our proposed method are as follows.(1) The propagation of high-level features which usually have relatively strong semantic information is enhanced in the decoding procedure.(2) With the help of PCSP, the gradient vanishing issues caused by non-linear operations in top-down information flows can be alleviated.(3) Thanks to the full exploration of high-level features, the decoding process of our method is relatively memory-efficient compared to those of existing methods. Integrating DNTDF with EfficientN et, we construct a highly light-weighted SOD model, with very low computational complexity. To demonstrate the effectiveness of the proposed model, comprehensive experiments are conducted on six widely-used benchmark datasets. The comparisons to the most state-of-the-art methods as well as the carefully-designed baseline models verify our insights on the top-down flow modeling for SOD.

关键词： salient object detection top-down flow densely nested framework convolutional neural networks

基于数据挖掘的中药治疗鸡大肠杆菌病用药规律分析

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畜禽业 2024年第10期35卷 28-32页

作者：韩俊伟杜海燕新乡市农业综合行政执法支队河南新乡453000 新乡市动物疫病预防控制中心河南新乡453000

为探索中药治疗鸡大肠杆菌病遣方用药规律,提供鸡大肠杆菌病临床治疗新思路,综合运用数据库和统计分析等现代信息技术,对临床治疗鸡大肠杆菌病中药组方数据进行挖掘、频率频次统计、关联规则分析,筛选常用的中药和组方。结果表明,中药... 详细信息

为探索中药治疗鸡大肠杆菌病遣方用药规律,提供鸡大肠杆菌病临床治疗新思路,综合运用数据库和统计分析等现代信息技术,对临床治疗鸡大肠杆菌病中药组方数据进行挖掘、频率频次统计、关联规则分析,筛选常用的中药和组方。结果表明,中药组方的单味药使用频次较高的为黄连、黄芩、黄柏、白头翁、甘草;药性统计中以寒、温、平较多;五味中以苦、甘、辛较多;药物类别分类中以清热解毒,清热燥湿,补中益气类用药频率较高。药物归经中归胃经,大肠经,心经的中药居多。关联规则分析中支持度最高的中药组合是黄连-黄柏,置信度最高的中药组合为黄连-大黄和黄柏。通过对大量相关中药方剂信息分析挖掘,汇总中药组方规律,提炼疗效显著的中药组方数据为临床上治疗鸡大肠杆菌病提供参考。

关键词：中药鸡大肠杆菌病数据挖掘用药规律

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